pcl Namespace Reference

Namespaces
namespace	apps
namespace	common
namespace	ComparisonOps
namespace	console
namespace	detail
namespace	distances
namespace	fields
namespace	geometry
namespace	io
namespace	octree
namespace	poisson
namespace	registration
namespace	search
namespace	surface
namespace	test
namespace	texture_mapping
namespace	tracking
namespace	traits
namespace	utils
namespace	visualization
Classes
struct	_Axis
	A point structure representing an Axis using its normal coordinates. (SSE friendly) More...
struct	_Normal
	A point structure representing normal coordinates and the surface curvature estimate. (SSE friendly) More...
struct	_PointNormal
	A point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate. (SSE friendly) More...
struct	_PointSurfel
	A surfel, that is, a point structure representing Euclidean xyz coordinates, together with normal coordinates, a RGBA color, a radius, a confidence value and the surface curvature estimate. More...
struct	_PointWithRange
	A point structure representing Euclidean xyz coordinates, padded with an extra range float. More...
struct	_PointWithScale
	A point structure representing a 3-D position and scale. More...
struct	_PointWithViewpoint
struct	_PointXYZ
struct	_PointXYZHSV
struct	_PointXYZI
	A point structure representing Euclidean xyz coordinates, and the intensity value. More...
struct	_PointXYZINormal
	A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate. More...
struct	_PointXYZL
struct	_PointXYZRGB
struct	_PointXYZRGBA
	A point structure representing Euclidean xyz coordinates, and the RGBA color. More...
struct	_PointXYZRGBL
struct	_PointXYZRGBNormal
	A point structure representing Euclidean xyz coordinates, and the RGB color, together with normal coordinates and the surface curvature estimate. Due to historical reasons (PCL was first developed as a ROS package), the RGB information is packed into an integer and casted to a float. This is something we wish to remove in the near future, but in the meantime, the following code snippet should help you pack and unpack RGB colors in your PointXYZRGB structure: More...
struct	_ReferenceFrame
	A structure representing the Local Reference Frame of a point. More...
class	AdaptiveRangeCoder
	AdaptiveRangeCoder compression class More...
class	ApproximateVoxelGrid
	ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...
struct	Axis
class	BilateralFilter
	A bilateral filter implementation for point cloud data. Uses the intensity data channel. More...
class	BilateralUpsampling
	Bilateral filtering implementation, based on the following paper: * Kopf, Johannes and Cohen, Michael F. and Lischinski, Dani and Uyttendaele, Matt - Joint Bilateral Upsampling, * ACM Transations in Graphics, July 2007. More...
class	BivariatePolynomialT
	This represents a bivariate polynomial and provides some functionality for it. More...
struct	BorderDescription
	A structure to store if a point in a range image lies on a border between an obstacle and the background. More...
struct	Boundary
	A point structure representing a description of whether a point is lying on a surface boundary or not. More...
class	BoundaryEstimation
	BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion. The code makes use of the estimated surface normals at each point in the input dataset. More...
class	BoundaryEstimation< PointInT, PointNT, Eigen::MatrixXf >
	BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion. The code makes use of the estimated surface normals at each point in the input dataset. More...
class	ChannelProperties
	ChannelProperties stores the properties of each channel in a cloud, namely: More...
class	Clipper3D
	Base class for 3D clipper objects. More...
struct	cloud_show
struct	cloud_show_base
class	CloudProperties
	CloudProperties stores a list of optional point cloud properties such as: More...
class	CloudSurfaceProcessing
	CloudSurfaceProcessing represents the base class for algorithms that take a point cloud as an input and produce a new output cloud that has been modified towards a better surface representation. These types of algorithms include surface smoothing, hole filling, cloud upsampling etc. More...
class	Comparator
	Comparator is the base class for comparators that compare two points given some function. Currently intended for use with OrganizedConnectedComponentSegmentation. More...
class	ComparisonBase
	The (abstract) base class for the comparison object. More...
class	ComputeFailedException
class	ConditionalRemoval
	ConditionalRemoval filters data that satisfies certain conditions. More...
class	ConditionAnd
	AND condition. More...
class	ConditionBase
	Base condition class. More...
class	ConditionOr
	OR condition. More...
struct	CopyIfFieldExists
	A helper functor that can copy a specific value if the given field exists. More...
struct	Correspondence
	Correspondence represents a match between two entities (e.g., points, descriptors, etc). This is represesented via the indices of a source point and a target point, and the distance between them. More...
class	CropBox
	CropBox is a filter that allows the user to filter all the data inside of a given box. More...
class	CropBox< sensor_msgs::PointCloud2 >
	CropBox is a filter that allows the user to filter all the data inside of a given box. More...
class	CropHull
	Filter points that lie inside or outside a 3D closed surface or 2D closed polygon, as generated by the ConvexHull or ConcaveHull classes. More...
class	CustomPointRepresentation
	CustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point. More...
class	CVFHEstimation
	CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in: More...
class	DefaultFeatureRepresentation
	DefaulFeatureRepresentation extends PointRepresentation and is intended to be used when defining the default behavior for feature descriptor types (i.e., copy each element of each field into a float array). More...
class	DefaultPointRepresentation
	DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types. More...
class	DefaultPointRepresentation< FPFHSignature33 >
class	DefaultPointRepresentation< NormalBasedSignature12 >
class	DefaultPointRepresentation< PFHRGBSignature250 >
class	DefaultPointRepresentation< PFHSignature125 >
class	DefaultPointRepresentation< PointNormal >
class	DefaultPointRepresentation< PointXYZ >
class	DefaultPointRepresentation< PointXYZI >
class	DefaultPointRepresentation< PPFSignature >
class	DefaultPointRepresentation< ShapeContext >
class	DefaultPointRepresentation< SHOT1344 >
class	DefaultPointRepresentation< SHOT352 >
class	DefaultPointRepresentation< VFHSignature308 >
class	EarClipping
	The ear clipping triangulation algorithm. The code is inspired by Flavien Brebion implementation, which is in n^3 and does not handle holes. More...
class	EdgeAwarePlaneComparator
	EdgeAwarePlaneComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data. More...
class	ESFEstimation
	ESFEstimation estimates the ensemble of shape functions descriptors for a given point cloud dataset containing points. Shape functions are D2, D3, A3. For more information about the ESF descriptor, see: Walter Wohlkinger and Markus Vincze, "Ensemble of Shape Functions for 3D Object Classification", IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO), 2011 More...
struct	ESFSignature640
	A point structure representing the Ensemble of Shape Functions (ESF). More...
class	EuclideanClusterComparator
	EuclideanClusterComparator is a comparator used for finding clusters supported by planar surfaces. This needs to be run as a second pass after extracting planar surfaces, using MultiPlaneSegmentation for example. More...
class	EuclideanClusterExtraction
	EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense. More...
class	EuclideanPlaneCoefficientComparator
	EuclideanPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data. More...
class	ExtractIndices
	ExtractIndices extracts a set of indices from a point cloud. More...
class	ExtractIndices< sensor_msgs::PointCloud2 >
	ExtractIndices extracts a set of indices from a point cloud. Usage examples: More...
class	ExtractPolygonalPrismData
	ExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given height, generates a 3D polygonal prism. The polygonal prism is then used to segment all points lying inside it. More...
class	Feature
	Feature represents the base feature class. Some generic 3D operations that are applicable to all features are defined here as static methods. More...
class	FeatureFromLabels
class	FeatureFromNormals
class	FeatureWithLocalReferenceFrames
	FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which need a local reference frame at each input keypoint. More...
class	FieldComparison
	The field-based specialization of the comparison object. More...
struct	FieldMatches
class	FileReader
	Point Cloud Data (FILE) file format reader interface. Any (FILE) format file reader should implement its virtual methodes. More...
class	FileWriter
	Point Cloud Data (FILE) file format writer. Any (FILE) format file reader should implement its virtual methodes. More...
class	Filter
	Filter represents the base filter class. All filters must inherit from this interface. More...
class	Filter< sensor_msgs::PointCloud2 >
	Filter represents the base filter class. All filters must inherit from this interface. More...
class	FilterIndices
	FilterIndices represents the base class for filters that are about binary point removal. All derived classes have to implement the filter (PointCloud &output) and the filter (std::vector<int> &indices) methods. Ideally they also make use of the negative_, keep_organized_ and extract_removed_indices_ systems. The distinguishment between the negative_ and extract_removed_indices_ systems only makes sense if the class automatically filters non-finite entries in the filtering methods (recommended). More...
class	FilterIndices< sensor_msgs::PointCloud2 >
	FilterIndices represents the base class for filters that are about binary point removal. All derived classes have to implement the filter (PointCloud &output) and the filter (std::vector<int> &indices) methods. Ideally they also make use of the negative_, keep_organized_ and extract_removed_indices_ systems. The distinguishment between the negative_ and extract_removed_indices_ systems only makes sense if the class automatically filters non-finite entries in the filtering methods (recommended). More...
struct	for_each_type_impl
struct	for_each_type_impl< false >
class	FPFHEstimation
	FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals. More...
class	FPFHEstimation< PointInT, PointNT, Eigen::MatrixXf >
	FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals. More...
class	FPFHEstimationOMP
	FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard. More...
struct	FPFHSignature33
	A point structure representing the Fast Point Feature Histogram (FPFH). More...
struct	Functor
class	GaussianKernel
class	GeneralizedIterativeClosestPoint
	GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al. in http://www.stanford.edu/~avsegal/resources/papers/Generalized_ICP.pdf The approach is based on using anistropic cost functions to optimize the alignment after closest point assignments have been made. The original code uses GSL and ANN while in ours we use an eigen mapped BFGS and FLANN. More...
struct	GFPFHSignature16
	A point structure representing the GFPFH descriptor with 16 bins. More...
class	Grabber
	Grabber interface for PCL 1.x device drivers. More...
class	GreedyProjectionTriangulation
	GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections. It assumes locally smooth surfaces and relatively smooth transitions between areas with different point densities. More...
class	GridProjection
	Grid projection surface reconstruction method. More...
class	HarrisKeypoint3D
	HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals. More...
struct	Histogram
	A point structure representing an N-D histogram. More...
class	InitFailedException
	An exception thrown when init can not be performed should be used in all the PCLBase class inheritants. More...
class	IntegralImage2D
	Determines an integral image representation for a given organized data array. More...
class	IntegralImage2D< DataType, 1 >
	partial template specialization for integral images with just one channel. More...
class	IntegralImageNormalEstimation
	Surface normal estimation on organized data using integral images. More...
struct	IntegralImageTypeTraits
struct	IntegralImageTypeTraits< char >
struct	IntegralImageTypeTraits< float >
struct	IntegralImageTypeTraits< int >
struct	IntegralImageTypeTraits< short >
struct	IntegralImageTypeTraits< unsigned char >
struct	IntegralImageTypeTraits< unsigned int >
struct	IntegralImageTypeTraits< unsigned short >
struct	IntensityGradient
	A point structure representing the intensity gradient of an XYZI point cloud. More...
class	IntensityGradientEstimation
	IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values. The intensity gradient at a given point will be a vector orthogonal to the surface normal and pointing in the direction of the greatest increase in local intensity; the vector's magnitude indicates the rate of intensity change. More...
class	IntensityGradientEstimation< PointInT, PointNT, Eigen::MatrixXf >
	IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values. The intensity gradient at a given point will be a vector orthogonal to the surface normal and pointing in the direction of the greatest increase in local intensity; the vector's magnitude indicates the rate of intensity change. More...
class	IntensitySpinEstimation
	IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity. For more information about the intensity-domain spin image descriptor, see: More...
class	IntensitySpinEstimation< PointInT, Eigen::MatrixXf >
	IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity. For more information about the intensity-domain spin image descriptor, see: More...
struct	InterestPoint
	A point structure representing an interest point with Euclidean xyz coordinates, and an interest value. More...
struct	intersect
class	InvalidConversionException
	An exception that is thrown when a PointCloud2 message cannot be converted into a PCL type. More...
class	InvalidSACModelTypeException
	An exception that is thrown when a sample consensus model doesn't have the correct number of samples defined in model_types.h. More...
class	IOException
	An exception that is thrown during an IO error (typical read/write errors) More...
class	IsNotDenseException
	An exception that is thrown when a PointCloud is not dense but is attemped to be used as dense. More...
class	IterativeClosestPoint
	IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm. The transformation is estimated based on Singular Value Decomposition (SVD). More...
class	IterativeClosestPointNonLinear
	IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend. The resultant transformation is optimized as a quaternion. More...
class	KdTree
	KdTree represents the base spatial locator class for kd-tree implementations. More...
class	KdTreeFLANN
	KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. The class is making use of the FLANN (Fast Library for Approximate Nearest Neighbor) project by Marius Muja and David Lowe. More...
class	KdTreeFLANN< Eigen::MatrixXf >
	KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. The class is making use of the FLANN (Fast Library for Approximate Nearest Neighbor) project by Marius Muja and David Lowe. More...
class	KernelWidthTooSmallException
	An exception that is thrown when the kernel size is too small. More...
class	Keypoint
	Keypoint represents the base class for key points. More...
struct	Label
class	LabeledEuclideanClusterExtraction
	LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info. More...
class	LeastMedianSquares
	LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm. LMedS is a RANSAC-like model-fitting algorithm that can tolerate up to 50% outliers without requiring thresholds to be set. See Andrea Fusiello's "Elements of Geometric Computer Vision" (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial.html#x1-520007) for more details. More...
class	LineIterator
	Organized Index Iterator for iterating over the "pixels" for a given line using the Bresenham algorithm. Supports 4 and 8 neighborhood connectivity. More...
class	MarchingCubes
	The marching cubes surface reconstruction algorithm. This is an abstract class that takes a grid and extracts the isosurface as a mesh, based on the original marching cubes paper: More...
class	MarchingCubesHoppe
	The marching cubes surface reconstruction algorithm, using a signed distance function based on the distance from tangent planes, proposed by Hoppe et. al. in: Hoppe H., DeRose T., Duchamp T., MC-Donald J., Stuetzle W., "Surface reconstruction from unorganized points", SIGGRAPH '92. More...
class	MarchingCubesRBF
	The marching cubes surface reconstruction algorithm, using a signed distance function based on radial basis functions. Partially based on: Carr J.C., Beatson R.K., Cherrie J.B., Mitchell T.J., Fright W.R., McCallum B.C. and Evans T.R., "Reconstruction and representation of 3D objects with radial basis functions" SIGGRAPH '01. More...
class	MaximumLikelihoodSampleConsensus
	MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S. Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000. More...
class	MeshConstruction
	MeshConstruction represents a base surface reconstruction class. All mesh constructing methods that take in a point cloud and generate a surface that uses the original data as vertices should inherit from this class. More...
class	MeshProcessing
	MeshProcessing represents the base class for mesh processing algorithms. More...
class	MeshSmoothingLaplacianVTK
	PCL mesh smoothing based on the vtkSmoothPolyDataFilter algorithm from the VTK library. Please check out the original documentation for more details on the inner workings of the algorithm Warning: This wrapper does two fairly computationally expensive conversions from the PCL PolygonMesh data structure to the vtkPolyData data structure and back. More...
class	MeshSmoothingWindowedSincVTK
	PCL mesh smoothing based on the vtkWindowedSincPolyDataFilter algorithm from the VTK library. Please check out the original documentation for more details on the inner workings of the algorithm Warning: This wrapper does two fairly computationally expensive conversions from the PCL PolygonMesh data structure to the vtkPolyData data structure and back. More...
class	MeshSubdivisionVTK
	PCL mesh smoothing based on the vtkLinearSubdivisionFilter, vtkLoopSubdivisionFilter, vtkButterflySubdivisionFilter depending on the selected MeshSubdivisionVTKFilterType algorithm from the VTK library. Please check out the original documentation for more details on the inner workings of the algorithm Warning: This wrapper does two fairly computationally expensive conversions from the PCL PolygonMesh data structure to the vtkPolyData data structure and back. More...
class	MEstimatorSampleConsensus
	MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S. Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000. More...
struct	MomentInvariants
	A point structure representing the three moment invariants. More...
class	MomentInvariantsEstimation
	MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point. More...
class	MomentInvariantsEstimation< PointInT, Eigen::MatrixXf >
	MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point. More...
class	MovingLeastSquares
	MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation. It also contains methods for upsampling the resulting cloud based on the parametric fit. Reference paper: "Computing and Rendering Point Set Surfaces" by Marc Alexa, Johannes Behr, Daniel Cohen-Or, Shachar Fleishman, David Levin and Claudio T. Silva www.sci.utah.edu/~shachar/Publications/crpss.pdf. More...
class	MovingLeastSquaresOMP
	MovingLeastSquaresOMP represent an OpenMP implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation. More...
class	MultiscaleFeaturePersistence
	Generic class for extracting the persistent features from an input point cloud It can be given any Feature estimator instance and will compute the features of the input over a multiscale representation of the cloud and output the unique ones over those scales. More...
class	Narf
	NARF (Normal Aligned Radial Features) is a point feature descriptor type for 3D data. Please refer to pcl/features/narf_descriptor.h if you want the class derived from pcl Feature. More...
struct	Narf36
	A point structure representing the Narf descriptor. More...
class	NarfDescriptor
class	NarfKeypoint
	NARF (Normal Aligned Radial Feature) keypoints. Input is a range image, output the indices of the keypoints More...
struct	NdCentroidFunctor
	Helper functor structure for n-D centroid estimation. More...
struct	NdConcatenateFunctor
	Helper functor structure for concatenate. More...
struct	NdCopyEigenPointFunctor
	Helper functor structure for copying data between an Eigen type and a PointT. More...
struct	NdCopyPointEigenFunctor
	Helper functor structure for copying data between an Eigen type and a PointT. More...
class	NNClassification
	Nearest neighbor search based classification of PCL point type features. FLANN is used to identify a neighborhood, based on which different scoring schemes can be employed to obtain likelihood values for a specified list of classes. More...
struct	Normal
struct	NormalBasedSignature12
	A point structure representing the Normal Based Signature for a feature matrix of 4-by-3. More...
class	NormalBasedSignatureEstimation
	Normal-based feature signature estimation class. Obtains the feature vector by applying Discrete Cosine and Fourier Transforms on an NxM array of real numbers representing the projection distances of the points in the input cloud to a disc around the point of interest. Please consult the following publication for more details: Xinju Li and Igor Guskov Multi-scale features for approximate alignment of point-based surfaces Proceedings of the third Eurographics symposium on Geometry processing July 2005, Vienna, Austria. More...
class	NormalEstimation
	NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point. If PointOutT is specified as pcl::Normal, the normal is stored in the first 3 components (0-2), and the curvature is stored in component 3. More...
class	NormalEstimation< PointInT, Eigen::MatrixXf >
	NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures. More...
class	NormalEstimationOMP
	NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard. More...
class	NormalEstimationOMP< PointInT, Eigen::MatrixXf >
	NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard. More...
class	NormalSpaceSampling
	NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every point. More...
class	NotEnoughPointsException
	An exception that is thrown when the number of correspondants is not equal to the minimum required. More...
class	OrganizedConnectedComponentSegmentation
	OrganizedConnectedComponentSegmentation allows connected components to be found within organized point cloud data, given a comparison function. Given an input cloud and a comparator, it will output a PointCloud of labels, giving each connected component a unique id, along with a vector of PointIndices corresponding to each component. See OrganizedMultiPlaneSegmentation for an example application. More...
class	OrganizedFastMesh
	Simple triangulation/surface reconstruction for organized point clouds. Neighboring points (pixels in image space) are connected to construct a triangular mesh. More...
class	OrganizedIndexIterator
	base class for iterators on 2-dimensional maps like images/organized clouds etc. More...
class	OrganizedMultiPlaneSegmentation
	OrganizedMultiPlaneSegmentation finds all planes present in the input cloud, and outputs a vector of plane equations, as well as a vector of point clouds corresponding to the inliers of each detected plane. Only planes with more than min_inliers points are detected. Templated on point type, normal type, and label type. More...
class	PackedHSIComparison
	A packed HSI specialization of the comparison object. More...
class	PackedRGBComparison
	A packed rgb specialization of the comparison object. More...
class	PassThrough
	PassThrough passes points in a cloud based on constraints for one particular field of the point type. More...
class	PassThrough< sensor_msgs::PointCloud2 >
	PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints. More...
class	PCA
class	PCDGrabber
class	PCDGrabberBase
	Base class for PCD file grabber. More...
class	PCDReader
	Point Cloud Data (PCD) file format reader. More...
class	PCDWriter
	Point Cloud Data (PCD) file format writer. More...
class	PCLBase
	PCL base class. Implements methods that are used by all PCL objects. More...
class	PCLBase< sensor_msgs::PointCloud2 >
class	PCLException
	A base class for all pcl exceptions which inherits from std::runtime_error. More...
class	PCLIOException
	Base exception class for I/O operations. More...
class	PCLSurfaceBase
	Pure abstract class. All types of meshing/reconstruction algorithms in libpcl_surface must inherit from this, in order to make sure we have a consistent API. The methods that we care about here are: More...
class	PFHEstimation
	PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals. More...
class	PFHEstimation< PointInT, PointNT, Eigen::MatrixXf >
	PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals. More...
class	PFHRGBEstimation
struct	PFHRGBSignature250
	A point structure representing the Point Feature Histogram with colors (PFHRGB). More...
struct	PFHSignature125
	A point structure representing the Point Feature Histogram (PFH). More...
class	PiecewiseLinearFunction
	This provides functionalities to efficiently return values for piecewise linear function. More...
class	PlanarPolygon
	PlanarPolygon represents a planar (2D) polygon, potentially in a 3D space. More...
class	PlanarPolygonFusion
	PlanarPolygonFusion takes a list of 2D planar polygons and attempts to reduce them to a minimum set that best represents the scene, based on various given comparators. More...
class	PlanarRegion
	PlanarRegion represents a set of points that lie in a plane. Inherits summary statistics about these points from Region3D, and summary statistics of a 3D collection of points. More...
class	PlaneClipper3D
	Implementation of a plane clipper in 3D. More...
class	PlaneCoefficientComparator
	PlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data. More...
class	PlaneRefinementComparator
	PlaneRefinementComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data. More...
class	PLYReader
	Point Cloud Data (PLY) file format reader. More...
class	PLYWriter
	Point Cloud Data (PLY) file format writer. More...
class	PointCloud
	PointCloud represents the base class in PCL for storing collections of 3D points. More...
class	PointCloud< Eigen::MatrixXf >
	PointCloud specialization for Eigen matrices. For advanced users only! More...
struct	PointCorrespondence3D
	Representation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g. from feature matching) More...
struct	PointCorrespondence6D
	Representation of a (possible) correspondence between two points (e.g. from feature matching), that encode complete 6DOF transoformations. More...
class	PointDataAtOffset
	A datatype that enables type-correct comparisons. More...
struct	PointNormal
class	PointRepresentation
	PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector. More...
struct	PointSurfel
struct	PointWithRange
struct	PointWithScale
struct	PointWithViewpoint
	A point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen. More...
struct	PointXY
	A 2D point structure representing Euclidean xy coordinates. More...
struct	PointXYZ
	A point structure representing Euclidean xyz coordinates. (SSE friendly) More...
struct	PointXYZHSV
struct	PointXYZI
struct	PointXYZINormal
struct	PointXYZL
struct	PointXYZRGB
	A point structure representing Euclidean xyz coordinates, and the RGB color. More...
struct	PointXYZRGBA
struct	PointXYZRGBL
struct	PointXYZRGBNormal
class	Poisson
	The Poisson surface reconstruction algorithm. More...
class	PolynomialCalculationsT
	This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials. More...
class	PosesFromMatches
	calculate 3D transformation based on point correspondencdes More...
class	PPFEstimation
	Class that calculates the "surflet" features for each pair in the given pointcloud. Please refer to the following publication for more details: B. Drost, M. Ulrich, N. Navab, S. Ilic Model Globally, Match Locally: Efficient and Robust 3D Object Recognition 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 13-18 June 2010, San Francisco, CA. More...
class	PPFEstimation< PointInT, PointNT, Eigen::MatrixXf >
	Class that calculates the "surflet" features for each pair in the given pointcloud. Please refer to the following publication for more details: B. Drost, M. Ulrich, N. Navab, S. Ilic Model Globally, Match Locally: Efficient and Robust 3D Object Recognition 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 13-18 June 2010, San Francisco, CA. More...
class	PPFHashMapSearch
class	PPFRegistration
	Class that registers two point clouds based on their sets of PPFSignatures. Please refer to the following publication for more details: B. Drost, M. Ulrich, N. Navab, S. Ilic Model Globally, Match Locally: Efficient and Robust 3D Object Recognition 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 13-18 June 2010, San Francisco, CA. More...
class	PPFRGBEstimation
class	PPFRGBRegionEstimation
struct	PPFRGBSignature
	A point structure for storing the Point Pair Color Feature (PPFRGB) values. More...
struct	PPFSignature
	A point structure for storing the Point Pair Feature (PPF) values. More...
struct	PrincipalCurvatures
	A point structure representing the principal curvatures and their magnitudes. More...
class	PrincipalCurvaturesEstimation
	PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals. More...
class	PrincipalCurvaturesEstimation< PointInT, PointNT, Eigen::MatrixXf >
	PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals. More...
struct	PrincipalRadiiRSD
	A point structure representing the minimum and maximum surface radii (in meters) computed using RSD. More...
class	ProgressiveSampleConsensus
	RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Matching with PROSAC – Progressive Sample Consensus", Chum, O. and Matas, J.G., CVPR, I: 220-226 2005. More...
class	ProjectInliers
	ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud. More...
class	ProjectInliers< sensor_msgs::PointCloud2 >
	ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud. More...
class	PyramidFeatureHistogram
	Class that compares two sets of features by using a multiscale representation of the features inside a pyramid. Each level of the pyramid offers information about the similarity of the two feature sets. More...
class	RadiusOutlierRemoval
	RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have. More...
class	RadiusOutlierRemoval< sensor_msgs::PointCloud2 >
	RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K. More...
class	RandomizedMEstimatorSampleConsensus
	RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus). More...
class	RandomizedRandomSampleConsensus
	RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RAndom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O. Chum and J. Matas, Proc. British Machine Vision Conf. (BMVC '02), vol. 2, BMVA, pp. 448-457, 2002. More...
class	RandomSample
	RandomSample applies a random sampling with uniform probability. Based off Algorithm A from the paper "Faster Methods for Random Sampling" by Jeffrey Scott Vitter. The algorithm runs in O(N) and results in sorted indices http://www.ittc.ku.edu/~jsv/Papers/Vit84.sampling.pdf More...
class	RandomSample< sensor_msgs::PointCloud2 >
	RandomSample applies a random sampling with uniform probability. More...
class	RandomSampleConsensus
	RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Martin A. Fischler and Robert C. Bolles, Comm. Of the ACM 24: 381–395, June 1981. More...
class	RangeImage
	RangeImage is derived from pcl/PointCloud and provides functionalities with focus on situations where a 3D scene was captured from a specific view point. More...
class	RangeImageBorderExtractor
	Extract obstacle borders from range images, meaning positions where there is a transition from foreground to background. More...
class	RangeImagePlanar
	RangeImagePlanar is derived from the original range image and differs from it because it's not a spherical projection, but using a projection plane (as normal cameras do), therefore being better applicable for range sensors that already provide a range image by themselves (stereo cameras, ToF-cameras), so that a conversion to point cloud and then to a spherical range image becomes unnecessary. More...
struct	ReferenceFrame
class	Region3D
	Region3D represents summary statistics of a 3D collection of points. More...
class	Registration
	Registration represents the base registration class. All 3D registration methods should inherit from this class. More...
class	RegistrationVisualizer
	RegistrationVisualizer represents the base class for rendering the intermediate positions ocupied by the source point cloud during it's registration to the target point cloud. A registration algorithm is considered as input and it's covergence is rendered. More...
struct	RGB
	A structure representing RGB color information. More...
class	RGBPlaneCoefficientComparator
	RGBPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. Also takes into account RGB, so we can segmented different colored co-planar regions. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data. More...
class	RIFTEstimation
	RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity. For more information about the RIFT descriptor, see: More...
class	RIFTEstimation< PointInT, GradientT, Eigen::MatrixXf >
	RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity. For more information about the RIFT descriptor, see: More...
class	RSDEstimation
	RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves) for a given point cloud dataset containing points and normals. More...
class	SACSegmentation
	SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation. More...
class	SACSegmentationFromNormals
	SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation. More...
class	SampleConsensus
	SampleConsensus represents the base class. All sample consensus methods must inherit from this class. More...
class	SampleConsensusInitialAlignment
	SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al. More...
class	SampleConsensusModel
	SampleConsensusModel represents the base model class. All sample consensus models must inherit from this class. More...
class	SampleConsensusModelCircle2D
	SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane. More...
class	SampleConsensusModelCone
	SampleConsensusModelCone defines a model for 3D cone segmentation. The model coefficients are defined as: More...
class	SampleConsensusModelCylinder
	SampleConsensusModelCylinder defines a model for 3D cylinder segmentation. The model coefficients are defined as: More...
class	SampleConsensusModelFromNormals
	SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation. More...
class	SampleConsensusModelLine
	SampleConsensusModelLine defines a model for 3D line segmentation. The model coefficients are defined as: More...
class	SampleConsensusModelNormalParallelPlane
	SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints. Basically this means that checking for inliers will not only involve a "distance to model" criterion, but also an additional "maximum angular deviation" between the plane's normal and the inlier points normals. In addition, the plane normal must lie parallel to an user-specified axis. More...
class	SampleConsensusModelNormalPlane
	SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints. Basically this means that checking for inliers will not only involve a "distance to model" criterion, but also an additional "maximum angular deviation" between the plane's normal and the inlier points normals. More...
class	SampleConsensusModelNormalSphere
	SampleConsensusModelNormalSphere defines a model for 3D sphere segmentation using additional surface normal constraints. Basically this means that checking for inliers will not only involve a "distance to model" criterion, but also an additional "maximum angular deviation" between the sphere's normal and the inlier points normals. More...
class	SampleConsensusModelParallelLine
	SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints. The model coefficients are defined as: More...
class	SampleConsensusModelParallelPlane
	SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints. The plane must be parallel to a user-specified axis (setAxis) within an user-specified angle threshold (setEpsAngle). More...
class	SampleConsensusModelPerpendicularPlane
	SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints. The plane must be perpendicular to an user-specified axis (setAxis), up to an user-specified angle threshold (setEpsAngle). The model coefficients are defined as: More...
class	SampleConsensusModelPlane
	SampleConsensusModelPlane defines a model for 3D plane segmentation. The model coefficients are defined as: More...
class	SampleConsensusModelRegistration
	SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection. More...
class	SampleConsensusModelSphere
	SampleConsensusModelSphere defines a model for 3D sphere segmentation. The model coefficients are defined as: More...
class	SampleConsensusModelStick
	SampleConsensusModelStick defines a model for 3D stick segmentation. A stick is a line with an user given minimum/maximum width. The model coefficients are defined as: More...
class	ScopeTime
	Class to measure the time spent in a scope. More...
class	SegmentDifferences
	SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold. More...
struct	SetIfFieldExists
	A helper functor that can set a specific value in a field if the field exists. More...
struct	ShapeContext
	A point structure representing a Shape Context. More...
class	ShapeContext3DEstimation
	ShapeContext3DEstimation implements the 3D shape context descriptor as described in: More...
class	ShapeContext3DEstimation< PointInT, PointNT, Eigen::MatrixXf >
	ShapeContext3DEstimation implements the 3D shape context descriptor as described in: More...
struct	SHOT
	A point structure representing the generic Signature of Histograms of OrienTations (SHOT). More...
struct	SHOT1344
	A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape+color. More...
struct	SHOT352
	A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape only. More...
class	SHOTColorEstimation
	SHOTColorEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors. More...
class	SHOTColorEstimation< PointInT, PointNT, Eigen::MatrixXf, PointRFT >
class	SHOTColorEstimationOMP
class	SHOTEstimation
	SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals. More...
class	SHOTEstimation< PointInT, PointNT, Eigen::MatrixXf, PointRFT >
	SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals. More...
class	SHOTEstimationBase
	SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals. More...
class	SHOTEstimationOMP
	SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard. More...
class	SHOTLocalReferenceFrameEstimation
	SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor. More...
class	SHOTLocalReferenceFrameEstimationOMP
	SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor. More...
class	SIFTKeypoint
	SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity. This implementation adapts the original algorithm from images to point clouds. More...
struct	SIFTKeypointFieldSelector
struct	SIFTKeypointFieldSelector< PointNormal >
struct	SIFTKeypointFieldSelector< PointXYZ >
struct	SIFTKeypointFieldSelector< PointXYZRGB >
struct	SIFTKeypointFieldSelector< PointXYZRGBA >
class	SmoothedSurfacesKeypoint
	Based on the paper: Xinju Li and Igor Guskov Multi-scale features for approximate alignment of point-based surfaces Proceedings of the third Eurographics symposium on Geometry processing July 2005, Vienna, Austria. More...
class	SolverDidntConvergeException
	An exception that is thrown when the non linear solver didn't converge. More...
class	SpinImageEstimation
	Estimates spin-image descriptors in the given input points. More...
class	SpinImageEstimation< PointInT, PointNT, Eigen::MatrixXf >
	Estimates spin-image descriptors in the given input points. More...
class	StaticRangeCoder
	StaticRangeCoder compression class More...
class	StatisticalMultiscaleInterestRegionExtraction
	Class for extracting interest regions from unstructured point clouds, based on a multi scale statistical approach. Please refer to the following publications for more details: Ranjith Unnikrishnan and Martial Hebert Multi-Scale Interest Regions from Unorganized Point Clouds Workshop on Search in 3D (S3D), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) June, 2008. More...
class	StatisticalOutlierRemoval
	StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. More...
class	StatisticalOutlierRemoval< sensor_msgs::PointCloud2 >
	StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check: More...
class	StopWatch
	Simple stopwatch. More...
class	SurfaceReconstruction
	SurfaceReconstruction represents a base surface reconstruction class. All surface reconstruction methods take in a point cloud and generate a new surface from it, by either re-sampling the data or generating new data altogether. These methods are thus not preserving the topology of the original data. More...
class	SurfelSmoothing
class	Synchronizer
struct	TexMaterial
class	TextureMapping
	The texture mapping algorithm. More...
struct	TextureMesh
class	TfQuadraticXYZComparison
	A comparison whether the (x,y,z) components of a given point satisfy (p'Ap + 2v'p + c [OP] 0). Here [OP] stands for the defined pcl::ComparisonOps, i.e. for GT, GE, LT, LE or EQ; p = (x,y,z) is a point of the point cloud; A is 3x3 matrix; v is the 3x1 vector; c is a scalar. More...
class	TimeTrigger
	Timer class that invokes registered callback methods periodically. More...
class	TransformationFromCorrespondences
	Calculates a transformation based on corresponding 3D points. More...
class	UnhandledPointTypeException
class	UniformSampling
	UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...
class	UniqueShapeContext
	UniqueShapeContext implements the Unique Shape Descriptor described here: More...
class	UniqueShapeContext< PointInT, Eigen::MatrixXf, PointRFT >
	UniqueShapeContext implements the Unique Shape Descriptor described here: More...
class	UnorganizedPointCloudException
	An exception that is thrown when an organized point cloud is needed but not provided. More...
class	VectorAverage
	Calculates the weighted average and the covariance matrix. More...
class	VFHClassifierNN
	Utility class for nearest neighbor search based classification of VFH features. More...
class	VFHEstimation
	VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals. The default VFH implementation uses 45 binning subdivisions for each of the three extended FPFH values, plus another 45 binning subdivisions for the distances between each point and the centroid and 128 binning subdivisions for the viewpoint component, which results in a 308-byte array of float values. These are stored in a pcl::VFHSignature308 point type. A major difference between the PFH/FPFH descriptors and VFH, is that for a given point cloud dataset, only a single VFH descriptor will be estimated (vfhs->points.size() should be 1), while the resultant PFH/FPFH data will have the same number of entries as the number of points in the cloud. More...
struct	VFHSignature308
	A point structure representing the Viewpoint Feature Histogram (VFH). More...
class	VoxelGrid
	VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...
class	VoxelGrid< sensor_msgs::PointCloud2 >
	VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...
class	VTKUtils
class	WarpPointRigid
class	WarpPointRigid3D
class	WarpPointRigid6D
struct	xNdCopyEigenPointFunctor
	Helper functor structure for copying data between an Eigen::VectorXf and a PointT. More...
struct	xNdCopyPointEigenFunctor
	Helper functor structure for copying data between an Eigen::VectorXf and a PointT. More...
Typedefs
typedef Eigen::Map < Eigen::Array3f >	Array3fMap
typedef const Eigen::Map < const Eigen::Array3f >	Array3fMapConst
typedef Eigen::Map < Eigen::Array4f, Eigen::Aligned >	Array4fMap
typedef const Eigen::Map < const Eigen::Array4f, Eigen::Aligned >	Array4fMapConst
typedef BivariatePolynomialT < float >	BivariatePolynomial
typedef BivariatePolynomialT < double >	BivariatePolynomiald
typedef std::bitset< 32 >	BorderTraits
	Data type to store extended information about a transition from foreground to backgroundSpecification of the fields for BorderDescription::traits.
typedef pcl::PointCloud < pcl::PointXYZRGB >	cc
typedef pcl::PointCloud < pcl::PointXYZRGBA >	cca
typedef std::vector < pcl::Correspondence, Eigen::aligned_allocator < pcl::Correspondence > >	Correspondences
typedef boost::shared_ptr < const Correspondences >	CorrespondencesConstPtr
typedef boost::shared_ptr < Correspondences >	CorrespondencesPtr
typedef pcl::PointCloud < pcl::PointXYZI >	gc
typedef boost::shared_ptr < const std::vector< int > >	IndicesConstPtr
typedef boost::shared_ptr < std::vector< int > >	IndicesPtr
typedef pcl::PointCloud < pcl::PointXYZ >	mc
typedef std::vector < detail::FieldMapping >	MsgFieldMap
typedef std::vector < PointCorrespondence3D, Eigen::aligned_allocator < PointCorrespondence3D > >	PointCorrespondences3DVector
typedef std::vector < PointCorrespondence6D, Eigen::aligned_allocator < PointCorrespondence6D > >	PointCorrespondences6DVector
typedef PolynomialCalculationsT< float >	PolynomialCalculations
typedef PolynomialCalculationsT < double >	PolynomialCalculationsd
typedef boost::shared_ptr < pcl::TextureMesh const >	TextureMeshConstPtr
typedef boost::shared_ptr < pcl::TextureMesh >	TextureMeshPtr
typedef Eigen::Map < Eigen::Vector3f >	Vector3fMap
typedef const Eigen::Map < const Eigen::Vector3f >	Vector3fMapConst
typedef Eigen::Map < Eigen::Vector4f, Eigen::Aligned >	Vector4fMap
typedef const Eigen::Map < const Eigen::Vector4f, Eigen::Aligned >	Vector4fMapConst
typedef VectorAverage< float, 2 >	VectorAverage2f
typedef VectorAverage< float, 3 >	VectorAverage3f
typedef VectorAverage< float, 4 >	VectorAverage4f
Enumerations
enum	BorderTrait {   BORDER_TRAIT__OBSTACLE_BORDER, BORDER_TRAIT__SHADOW_BORDER, BORDER_TRAIT__VEIL_POINT, BORDER_TRAIT__SHADOW_BORDER_TOP,   BORDER_TRAIT__SHADOW_BORDER_RIGHT, BORDER_TRAIT__SHADOW_BORDER_BOTTOM, BORDER_TRAIT__SHADOW_BORDER_LEFT, BORDER_TRAIT__OBSTACLE_BORDER_TOP,   BORDER_TRAIT__OBSTACLE_BORDER_RIGHT, BORDER_TRAIT__OBSTACLE_BORDER_BOTTOM, BORDER_TRAIT__OBSTACLE_BORDER_LEFT, BORDER_TRAIT__VEIL_POINT_TOP,   BORDER_TRAIT__VEIL_POINT_RIGHT, BORDER_TRAIT__VEIL_POINT_BOTTOM, BORDER_TRAIT__VEIL_POINT_LEFT }
	Specification of the fields for BorderDescription::traits. More...
enum	NormType {   L1, L2_SQR, L2, LINF,   JM, B, SUBLINEAR, CS,   DIV, PF, K, KL,   HIK }
	Enum that defines all the types of norms available. More...
enum	SacModel {   SACMODEL_PLANE, SACMODEL_LINE, SACMODEL_CIRCLE2D, SACMODEL_CIRCLE3D,   SACMODEL_SPHERE, SACMODEL_CYLINDER, SACMODEL_CONE, SACMODEL_TORUS,   SACMODEL_PARALLEL_LINE, SACMODEL_PERPENDICULAR_PLANE, SACMODEL_PARALLEL_LINES, SACMODEL_NORMAL_PLANE,   SACMODEL_NORMAL_SPHERE, SACMODEL_REGISTRATION, SACMODEL_PARALLEL_PLANE, SACMODEL_NORMAL_PARALLEL_PLANE,   SACMODEL_STICK }
Functions
template<typename PointT >
void	approximatePolygon (const PlanarPolygon< PointT > &polygon, PlanarPolygon< PointT > &approx_polygon, float threshold, bool refine=false, bool closed=true)
	see approximatePolygon2D
template<typename PointT >
void	approximatePolygon2D (const typename PointCloud< PointT >::VectorType &polygon, typename PointCloud< PointT >::VectorType &approx_polygon, float threshold, bool refine=false, bool closed=true)
	returns an approximate polygon to given 2D contour. Uses just X and Y values.
template<typename FloatVectorT >
float	B_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the B norm of the vector between two points.
bool	compareLabeledPointClusters (const pcl::PointIndices &a, const pcl::PointIndices &b)
	Sort clusters method (for std::sort).
bool	comparePointClusters (const pcl::PointIndices &a, const pcl::PointIndices &b)
	Sort clusters method (for std::sort).
template<typename PointT >
unsigned int	compute3DCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &centroid)
	Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
template<typename PointT >
unsigned int	compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Vector4f &centroid)
	Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.
template<typename PointT >
unsigned int	compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Vector4f &centroid)
	Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.
template<typename Matrix , typename Vector >
void	computeCorrespondingEigenVector (const Matrix &mat, const typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
	determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi definite input matrix
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix3f &covariance_matrix)
	Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix3f &covariance_matrix)
	Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix3f &covariance_matrix)
	Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix3d &covariance_matrix)
	Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix3d &covariance_matrix)
	Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix3d &covariance_matrix)
	Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute normalized the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of points in the point cloud. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.
template<typename PointT >
unsigned int	computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.
template<typename PointT >
unsigned int	computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
	Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.
template<typename PointT >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix3f &covariance_matrix, Eigen::Vector4f &centroid)
	Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix3f &covariance_matrix, Eigen::Vector4f &centroid)
	Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix3f &covariance_matrix, Eigen::Vector4f &centroid)
	Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix3d &covariance_matrix, Eigen::Vector4d &centroid)
	Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix3d &covariance_matrix, Eigen::Vector4d &centroid)
	Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix3d &covariance_matrix, Eigen::Vector4d &centroid)
	Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.
template<typename PointT >
void	computeNDCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::VectorXf &centroid)
	General, all purpose nD centroid estimation for a set of points using their indices.
template<typename PointT >
void	computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::VectorXf &centroid)
	General, all purpose nD centroid estimation for a set of points using their indices.
template<typename PointT >
void	computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::VectorXf &centroid)
	General, all purpose nD centroid estimation for a set of points using their indices.
PCL_EXPORTS bool	computePairFeatures (const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, float &f1, float &f2, float &f3, float &f4)
	Compute the 4-tuple representation containing the three angles and one distance between two points represented by Cartesian coordinates and normals.
template<typename PointT >
void	computePointNormal (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &plane_parameters, float &curvature)
	Compute the Least-Squares plane fit for a given set of points, and return the estimated plane parameters together with the surface curvature.
template<typename PointT >
void	computePointNormal (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Vector4f &plane_parameters, float &curvature)
	Compute the Least-Squares plane fit for a given set of points, using their indices, and return the estimated plane parameters together with the surface curvature.
PCL_EXPORTS bool	computePPFPairFeature (const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, float &f1, float &f2, float &f3, float &f4)
PCL_EXPORTS bool	computeRGBPairFeatures (const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4i &colors1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, const Eigen::Vector4i &colors2, float &f1, float &f2, float &f3, float &f4, float &f5, float &f6, float &f7)
template<typename Matrix , typename Roots >
void	computeRoots (const Matrix &m, Roots &roots)
	computes the roots of the characteristic polynomial of the input matrix m, which are the eigenvalues
template<typename Scalar , typename Roots >
void	computeRoots2 (const Scalar &b, const Scalar &c, Roots &roots)
	Compute the roots of a quadratic polynom x^2 + b*x + c = 0.
template<typename PointInT , typename PointNT , typename PointOutT >
Eigen::MatrixXf	computeRSD (boost::shared_ptr< const pcl::PointCloud< PointInT > > &surface, boost::shared_ptr< const pcl::PointCloud< PointNT > > &normals, const std::vector< int > &indices, double max_dist, int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram=false)
	Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals.
template<typename PointNT , typename PointOutT >
Eigen::MatrixXf	computeRSD (boost::shared_ptr< const pcl::PointCloud< PointNT > > &normals, const std::vector< int > &indices, const std::vector< float > &sqr_dists, double max_dist, int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram=false)
	Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals.
template<typename PointT >
pcl::PointCloud < pcl::VFHSignature308 >::Ptr	computeVFH (typename PointCloud< PointT >::ConstPtr cloud, double radius)
	Helper function to extract the VFH feature describing the given point cloud.
template<typename PointIn1T , typename PointIn2T , typename PointOutT >
void	concatenateFields (const pcl::PointCloud< PointIn1T > &cloud1_in, const pcl::PointCloud< PointIn2T > &cloud2_in, pcl::PointCloud< PointOutT > &cloud_out)
	Concatenate two datasets representing different fields.
PCL_EXPORTS bool	concatenateFields (const sensor_msgs::PointCloud2 &cloud1_in, const sensor_msgs::PointCloud2 &cloud2_in, sensor_msgs::PointCloud2 &cloud_out)
	Concatenate two datasets representing different fields.
PCL_EXPORTS bool	concatenatePointCloud (const sensor_msgs::PointCloud2 &cloud1, const sensor_msgs::PointCloud2 &cloud2, sensor_msgs::PointCloud2 &cloud_out)
	Concatenate two sensor_msgs::PointCloud2.
template<typename PointInT , typename PointOutT >
void	copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, pcl::PointCloud< PointOutT > &cloud_out)
	Copy all the fields from a given point cloud into a new point cloud.
PCL_EXPORTS void	copyPointCloud (const sensor_msgs::PointCloud2 &cloud_in, const std::vector< int > &indices, sensor_msgs::PointCloud2 &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
PCL_EXPORTS void	copyPointCloud (const sensor_msgs::PointCloud2 &cloud_in, sensor_msgs::PointCloud2 &cloud_out)
	Copy fields and point cloud data from cloud_in to cloud_out.
template<typename PointT >
void	copyPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
template<typename PointT >
void	copyPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int, Eigen::aligned_allocator< int > > &indices, pcl::PointCloud< PointT > &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
template<typename PointInT , typename PointOutT >
void	copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointOutT > &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
template<typename PointInT , typename PointOutT >
void	copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, const std::vector< int, Eigen::aligned_allocator< int > > &indices, pcl::PointCloud< PointOutT > &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
template<typename PointT >
void	copyPointCloud (const pcl::PointCloud< PointT > &cloud_in, const PointIndices &indices, pcl::PointCloud< PointT > &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
template<typename PointInT , typename PointOutT >
void	copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, const PointIndices &indices, pcl::PointCloud< PointOutT > &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
template<typename PointT >
void	copyPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< pcl::PointIndices > &indices, pcl::PointCloud< PointT > &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
template<typename PointInT , typename PointOutT >
void	copyPointCloud (const pcl::PointCloud< PointInT > &cloud_in, const std::vector< pcl::PointIndices > &indices, pcl::PointCloud< PointOutT > &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
template<typename Type >
void	copyStringValue (const std::string &st, sensor_msgs::PointCloud2 &cloud, unsigned int point_index, unsigned int field_idx, unsigned int fields_count)
	Copy one single value of type T (uchar, char, uint, int, float, double, ...) from a string.
template<>
void	copyStringValue< int8_t > (const std::string &st, sensor_msgs::PointCloud2 &cloud, unsigned int point_index, unsigned int field_idx, unsigned int fields_count)
template<>
void	copyStringValue< uint8_t > (const std::string &st, sensor_msgs::PointCloud2 &cloud, unsigned int point_index, unsigned int field_idx, unsigned int fields_count)
virtual void	copyToFloatArray (const SHOT &p, float *out) const
template<typename Type >
void	copyValueString (const sensor_msgs::PointCloud2 &cloud, const unsigned int point_index, const int point_size, const unsigned int field_idx, const unsigned int fields_count, std::ostream &stream)
	insers a value of type Type (uchar, char, uint, int, float, double, ...) into a stringstream.
template<>
void	copyValueString< int8_t > (const sensor_msgs::PointCloud2 &cloud, const unsigned int point_index, const int point_size, const unsigned int field_idx, const unsigned int fields_count, std::ostream &stream)
template<>
void	copyValueString< uint8_t > (const sensor_msgs::PointCloud2 &cloud, const unsigned int point_index, const int point_size, const unsigned int field_idx, const unsigned int fields_count, std::ostream &stream)
template<typename PointT >
void	createMapping (const std::vector< sensor_msgs::PointField > &msg_fields, MsgFieldMap &field_map)
template<typename FloatVectorT >
float	CS_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the CS norm of the vector between two points.
float	deg2rad (float alpha)
	Convert an angle from degrees to radians.
double	deg2rad (double alpha)
	Convert an angle from degrees to radians.
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const Eigen::Vector4f &centroid, pcl::PointCloud< PointT > &cloud_out)
	Subtract a centroid from a point cloud and return the de-meaned representation.
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, const Eigen::Vector4f &centroid, pcl::PointCloud< PointT > &cloud_out)
	Subtract a centroid from a point cloud and return the de-meaned representation.
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const Eigen::Vector4f &centroid, Eigen::MatrixXf &cloud_out)
	Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, const Eigen::Vector4f &centroid, Eigen::MatrixXf &cloud_out)
	Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, const Eigen::Vector4f &centroid, Eigen::MatrixXf &cloud_out)
	Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.
template<typename Matrix >
Matrix::Scalar	determinant3x3Matrix (const Matrix &matrix)
template<typename FloatVectorT >
float	Div_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the div norm of the vector between two points.
template<typename Matrix , typename Vector >
void	eigen22 (const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
	determine the smallest eigenvalue and its corresponding eigenvector
template<typename Matrix , typename Vector >
void	eigen22 (const Matrix &mat, Matrix &eigenvectors, Vector &eigenvalues)
	determine the smallest eigenvalue and its corresponding eigenvector
template<typename Matrix , typename Vector >
void	eigen33 (const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
	determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi definite input matrix
template<typename Matrix , typename Vector >
void	eigen33 (const Matrix &mat, Vector &evals)
	determines the eigenvalues of the symmetric positive semi definite input matrix
template<typename Matrix , typename Vector >
void	eigen33 (const Matrix &mat, Matrix &evecs, Vector &evals)
	determines the eigenvalues and corresponding eigenvectors of the symmetric positive semi definite input matrix
template<typename PointType1 , typename PointType2 >
float	euclideanDistance (const PointType1 &p1, const PointType2 &p2)
	Calculate the euclidean distance between the two given points.
template<typename PointT >
void	extractEuclideanClusters (const PointCloud< PointT > &cloud, const boost::shared_ptr< search::Search< PointT > > &tree, float tolerance, std::vector< PointIndices > &clusters, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())
	Decompose a region of space into clusters based on the Euclidean distance between points.
template<typename PointT >
void	extractEuclideanClusters (const PointCloud< PointT > &cloud, const std::vector< int > &indices, const boost::shared_ptr< search::Search< PointT > > &tree, float tolerance, std::vector< PointIndices > &clusters, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())
	Decompose a region of space into clusters based on the Euclidean distance between points.
template<typename PointT , typename Normal >
void	extractEuclideanClusters (const PointCloud< PointT > &cloud, const PointCloud< Normal > &normals, float tolerance, const boost::shared_ptr< KdTree< PointT > > &tree, std::vector< PointIndices > &clusters, double eps_angle, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())
	Decompose a region of space into clusters based on the euclidean distance between points, and the normal angular deviation.
template<typename PointT , typename Normal >
void	extractEuclideanClusters (const PointCloud< PointT > &cloud, const PointCloud< Normal > &normals, const std::vector< int > &indices, const boost::shared_ptr< KdTree< PointT > > &tree, float tolerance, std::vector< PointIndices > &clusters, double eps_angle, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())
	Decompose a region of space into clusters based on the euclidean distance between points, and the normal angular deviation.
template<typename PointT >
void	extractLabeledEuclideanClusters (const PointCloud< PointT > &cloud, const boost::shared_ptr< search::Search< PointT > > &tree, float tolerance, std::vector< std::vector< PointIndices > > &labeled_clusters, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)(), unsigned int max_label=(std::numeric_limits< int >::max))
	Decompose a region of space into clusters based on the Euclidean distance between points.
template<typename PointT , typename Scalar >
void	flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z, Eigen::Matrix< Scalar, 4, 1 > &normal)
	Flip (in place) the estimated normal of a point towards a given viewpoint.
template<typename PointT , typename Scalar >
void	flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z, Eigen::Matrix< Scalar, 3, 1 > &normal)
	Flip (in place) the estimated normal of a point towards a given viewpoint.
template<typename PointT >
void	flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z, float &nx, float &ny, float &nz)
	Flip (in place) the estimated normal of a point towards a given viewpoint.
template<typename Sequence , typename F >
void	for_each_type (F f)
template<typename PointT >
void	fromROSMsg (const sensor_msgs::PointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map)
	Convert a PointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
template<typename PointT >
void	fromROSMsg (const sensor_msgs::PointCloud2 &msg, pcl::PointCloud< PointT > &cloud)
	Convert a PointCloud2 binary data blob into a pcl::PointCloud<T> object.
Eigen::MatrixXi	getAllNeighborCellIndices ()
	Get the relative cell indices of all the 26 neighbors.
void	getAllPcdFilesInDirectory (const std::string &directory, std::vector< std::string > &file_names)
	Find all *.pcd files in the directory and return them sorted.
double	getAngle3D (const Eigen::Vector4f &v1, const Eigen::Vector4f &v2)
	Compute the smallest angle between two vectors in the [ 0, PI ) interval in 3D.
template<typename PointT >
void	getApproximateIndices (const typename pcl::PointCloud< PointT >::Ptr &cloud_in, const typename pcl::PointCloud< PointT >::Ptr &cloud_ref, std::vector< int > &indices)
	Get a set of approximate indices for a given point cloud into a reference point cloud. The coordinates of the two point clouds can differ. The method uses an internal KdTree for finding the closest neighbors from cloud_in in cloud_ref.
template<typename Point1T , typename Point2T >
void	getApproximateIndices (const typename pcl::PointCloud< Point1T >::Ptr &cloud_in, const typename pcl::PointCloud< Point2T >::Ptr &cloud_ref, std::vector< int > &indices)
	Get a set of approximate indices for a given point cloud into a reference point cloud. The coordinates of the two point clouds can differ. The method uses an internal KdTree for finding the closest neighbors from cloud_in in cloud_ref.
template<typename PointT >
double	getCircumcircleRadius (const PointT &pa, const PointT &pb, const PointT &pc)
	Compute the radius of a circumscribed circle for a triangle formed of three points pa, pb, and pc.
PCL_EXPORTS bool	getEigenAsPointCloud (Eigen::MatrixXf &in, sensor_msgs::PointCloud2 &out)
	Copy the XYZ dimensions from an Eigen MatrixXf into a sensor_msgs::PointCloud2 message.
void	getEulerAngles (const Eigen::Affine3f &t, float &roll, float &pitch, float &yaw)
	Extract the Euler angles (XYZ-convention) from the given transformation.
template<int N>
void	getFeaturePointCloud (const std::vector< Eigen::MatrixXf, Eigen::aligned_allocator< Eigen::MatrixXf > > &histograms2D, PointCloud< Histogram< N > > &histogramsPC)
	Transform a list of 2D matrices into a point cloud containing the values in a vector (Histogram<N>). Can be used to transform the 2D histograms obtained in RSDEstimation into a point cloud.
int	getFieldIndex (const sensor_msgs::PointCloud2 &cloud, const std::string &field_name)
	Get the index of a specified field (i.e., dimension/channel)
template<typename PointT >
int	getFieldIndex (const pcl::PointCloud< PointT > &cloud, const std::string &field_name, std::vector< sensor_msgs::PointField > &fields)
	Get the index of a specified field (i.e., dimension/channel)
template<typename PointT >
int	getFieldIndex (const std::string &field_name, std::vector< sensor_msgs::PointField > &fields)
	Get the index of a specified field (i.e., dimension/channel)
template<typename PointT >
void	getFields (const pcl::PointCloud< PointT > &cloud, std::vector< sensor_msgs::PointField > &fields)
	Get the list of available fields (i.e., dimension/channel)
template<typename PointT >
void	getFields (std::vector< sensor_msgs::PointField > &fields)
	Get the list of available fields (i.e., dimension/channel)
int	getFieldSize (const int datatype)
	Obtains the size of a specific field data type in bytes.
template<typename PointT >
std::string	getFieldsList (const pcl::PointCloud< PointT > &cloud)
	Get the list of all fields available in a given cloud.
std::string	getFieldsList (const sensor_msgs::PointCloud2 &cloud)
	Get the available point cloud fields as a space separated string.
PCL_EXPORTS void	getFieldsSizes (const std::vector< sensor_msgs::PointField > &fields, std::vector< int > &field_sizes)
	Obtain a vector with the sizes of all valid fields (e.g., not "_")
int	getFieldType (const int size, char type)
	Obtains the type of the PointField from a specific size and type.
char	getFieldType (const int type)
	Obtains the type of the PointField from a specific PointField as a char.
std::string	getFileExtension (const std::string &input)
	Get the file extension from the given string (the remaining string after the last '.')
std::string	getFilenameWithoutExtension (const std::string &input)
	Remove the extension from the given string and return only the filename (everything before the last '.')
std::string	getFilenameWithoutPath (const std::string &input)
	Remove the path from the given string and return only the filename (the remaining string after the last '/')
Eigen::MatrixXi	getHalfNeighborCellIndices ()
	Get the relative cell indices of the "upper half" 13 neighbors.
template<typename PointT >
void	getMaxDistance (const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
	Get the point at maximum distance from a given point and a given pointcloud.
template<typename PointT >
void	getMaxDistance (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
	Get the point at maximum distance from a given point and a given pointcloud.
template<typename PointT >
double	getMaxSegment (const pcl::PointCloud< PointT > &cloud, PointT &pmin, PointT &pmax)
	Obtain the maximum segment in a given set of points, and return the minimum and maximum points.
template<typename PointT >
double	getMaxSegment (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, PointT &pmin, PointT &pmax)
	Obtain the maximum segment in a given set of points, and return the minimum and maximum points.
void	getMeanStd (const std::vector< float > &values, double &mean, double &stddev)
	Compute both the mean and the standard deviation of an array of values.
PCL_EXPORTS void	getMeanStdDev (const std::vector< float > &values, double &mean, double &stddev)
	Compute both the mean and the standard deviation of an array of values.
template<typename PointT >
void	getMinMax (const PointT &histogram, int len, float &min_p, float &max_p)
	Get the minimum and maximum values on a point histogram.
PCL_EXPORTS void	getMinMax (const sensor_msgs::PointCloud2 &cloud, int idx, const std::string &field_name, float &min_p, float &max_p)
	Get the minimum and maximum values on a point histogram.
PCL_EXPORTS void	getMinMax3D (const sensor_msgs::PointCloud2ConstPtr &cloud, int x_idx, int y_idx, int z_idx, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt)
	Obtain the maximum and minimum points in 3D from a given point cloud.
PCL_EXPORTS void	getMinMax3D (const sensor_msgs::PointCloud2ConstPtr &cloud, int x_idx, int y_idx, int z_idx, const std::string &distance_field_name, float min_distance, float max_distance, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, bool limit_negative=false)
	Obtain the maximum and minimum points in 3D from a given point cloud.
template<typename PointT >
void	getMinMax3D (const pcl::PointCloud< PointT > &cloud, PointT &min_pt, PointT &max_pt)
	Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
template<typename PointT >
void	getMinMax3D (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt)
	Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
template<typename PointT >
void	getMinMax3D (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt)
	Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
template<typename PointT >
void	getMinMax3D (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt)
	Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
template<typename PointT >
void	getMinMax3D (const typename pcl::PointCloud< PointT >::ConstPtr &cloud, const std::string &distance_field_name, float min_distance, float max_distance, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, bool limit_negative=false)
	Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud, without considering points outside of a distance threshold from the laser origin.
PCL_EXPORTS bool	getPointCloudAsEigen (const sensor_msgs::PointCloud2 &in, Eigen::MatrixXf &out)
	Copy the XYZ dimensions of a sensor_msgs::PointCloud2 into Eigen format.
template<typename PointT >
void	getPointCloudDifference (const pcl::PointCloud< PointT > &src, const pcl::PointCloud< PointT > &tgt, double threshold, const boost::shared_ptr< pcl::search::Search< PointT > > &tree, pcl::PointCloud< PointT > &output)
	Obtain the difference between two aligned point clouds as another point cloud, given a distance threshold.
template<typename PointT >
void	getPointsInBox (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, std::vector< int > &indices)
	Get a set of points residing in a box given its bounds.
void	getRejectedQueryIndices (const pcl::Correspondences &correspondences_before, const pcl::Correspondences &correspondences_after, std::vector< int > &indices, bool presorting_required=true)
	Get the query points of correspondences that are present in one correspondence vector but not in the other, e.g., to compare correspondences before and after rejection.
double	getTime ()
void	getTransformation (float x, float y, float z, float roll, float pitch, float yaw, Eigen::Affine3f &t)
	Create a transformation from the given translation and Euler angles (XYZ-convention)
Eigen::Affine3f	getTransformation (float x, float y, float z, float roll, float pitch, float yaw)
	Create a transformation from the given translation and Euler angles (XYZ-convention)
void	getTransformationFromTwoUnitVectors (const Eigen::Vector3f &y_direction, const Eigen::Vector3f &z_axis, Eigen::Affine3f &transformation)
	Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
Eigen::Affine3f	getTransformationFromTwoUnitVectors (const Eigen::Vector3f &y_direction, const Eigen::Vector3f &z_axis)
	Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
void	getTransformationFromTwoUnitVectorsAndOrigin (const Eigen::Vector3f &y_direction, const Eigen::Vector3f &z_axis, const Eigen::Vector3f &origin, Eigen::Affine3f &transformation)
	Get the transformation that will translate orign to (0,0,0) and rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
void	getTransFromUnitVectorsXY (const Eigen::Vector3f &x_axis, const Eigen::Vector3f &y_direction, Eigen::Affine3f &transformation)
	Get the unique 3D rotation that will rotate x_axis into (1,0,0) and y_direction into a vector with z=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
Eigen::Affine3f	getTransFromUnitVectorsXY (const Eigen::Vector3f &x_axis, const Eigen::Vector3f &y_direction)
	Get the unique 3D rotation that will rotate x_axis into (1,0,0) and y_direction into a vector with z=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
void	getTransFromUnitVectorsZY (const Eigen::Vector3f &z_axis, const Eigen::Vector3f &y_direction, Eigen::Affine3f &transformation)
	Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
Eigen::Affine3f	getTransFromUnitVectorsZY (const Eigen::Vector3f &z_axis, const Eigen::Vector3f &y_direction)
	Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)
void	getTranslationAndEulerAngles (const Eigen::Affine3f &t, float &x, float &y, float &z, float &roll, float &pitch, float &yaw)
template<typename FloatVectorT >
float	HIK_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the HIK norm of the vector between two points.
template<typename Matrix >
Matrix::Scalar	invert2x2 (const Matrix &matrix, Matrix &inverse)
	Calculate the inverse of a 2x2 matrix.
template<typename Matrix >
Matrix::Scalar	invert3x3Matrix (const Matrix &matrix, Matrix &inverse)
	Calculate the inverse of a general 3x3 matrix.
template<typename Matrix >
Matrix::Scalar	invert3x3SymMatrix (const Matrix &matrix, Matrix &inverse)
	Calculate the inverse of a 3x3 symmetric matrix.
bool	isBetterCorrespondence (const Correspondence &pc1, const Correspondence &pc2)
	Comparator to enable us to sort a vector of PointCorrespondences according to their scores using std::sort (begin(), end(), isBetterCorrespondence);.
template<typename PointT >
bool	isFinite (const PointT &pt)
template<>
bool	isFinite< pcl::Normal > (const pcl::Normal &n)
template<typename PointT >
bool	isPointIn2DPolygon (const PointT &point, const pcl::PointCloud< PointT > &polygon)
	General purpose method for checking if a 3D point is inside or outside a given 2D polygon.
template<typename Type >
bool	isValueFinite (const sensor_msgs::PointCloud2 &cloud, const unsigned int point_index, const int point_size, const unsigned int field_idx, const unsigned int fields_count)
	Check whether a given value of type Type (uchar, char, uint, int, float, double, ...) is finite or not.
bool	isVisible (const Eigen::Vector2f &X, const Eigen::Vector2f &S1, const Eigen::Vector2f &S2, const Eigen::Vector2f &R=Eigen::Vector2f::Zero())
	Returns if a point X is visible from point R (or the origin) when taking into account the segment between the points S1 and S2.
template<typename PointT >
bool	isXYPointIn2DXYPolygon (const PointT &point, const pcl::PointCloud< PointT > &polygon)
	Check if a 2d point (X and Y coordinates considered only!) is inside or outside a given polygon. This method assumes that both the point and the polygon are projected onto the XY plane.
template<typename FloatVectorT >
float	JM_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the JM norm of the vector between two points.
template<typename FloatVectorT >
float	K_Norm (FloatVectorT A, FloatVectorT B, int dim, float P1, float P2)
	Compute the K norm of the vector between two points.
template<typename FloatVectorT >
float	KL_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the KL between two discrete probability density functions.
template<typename FloatVectorT >
float	L1_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the L1 norm of the vector between two points.
template<typename FloatVectorT >
float	L2_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the L2 norm of the vector between two points.
template<typename FloatVectorT >
float	L2_Norm_SQR (FloatVectorT A, FloatVectorT B, int dim)
	Compute the squared L2 norm of the vector between two points.
PCL_EXPORTS void	lineToLineSegment (const Eigen::VectorXf &line_a, const Eigen::VectorXf &line_b, Eigen::Vector4f &pt1_seg, Eigen::Vector4f &pt2_seg)
	Get the shortest 3D segment between two 3D lines.
PCL_EXPORTS bool	lineWithLineIntersection (const Eigen::VectorXf &line_a, const Eigen::VectorXf &line_b, Eigen::Vector4f &point, double sqr_eps=1e-4)
	Get the intersection of a two 3D lines in space as a 3D point.
PCL_EXPORTS bool	lineWithLineIntersection (const pcl::ModelCoefficients &line_a, const pcl::ModelCoefficients &line_b, Eigen::Vector4f &point, double sqr_eps=1e-4)
	Get the intersection of a two 3D lines in space as a 3D point.
template<typename FloatVectorT >
float	Linf_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the L-infinity norm of the vector between two points.
template<typename Derived >
void	loadBinary (Eigen::MatrixBase< Derived > const &matrix, std::istream &file)
	Read a matrix from an input stream.
PCL_EXPORTS unsigned int	lzfCompress (const void *const in_data, unsigned int in_len, void *out_data, unsigned int out_len)
	Compress in_len bytes stored at the memory block starting at in_data and write the result to out_data, up to a maximum length of out_len bytes using Marc Lehmann's LZF algorithm.
PCL_EXPORTS unsigned int	lzfDecompress (const void *const in_data, unsigned int in_len, void *out_data, unsigned int out_len)
	Decompress data compressed with the lzfCompress function and stored at location in_data and length in_len. The result will be stored at out_data up to a maximum of out_len characters.
float	normAngle (float alpha)
	Normalize an angle to (-PI, PI].
std::ostream &	operator<< (std::ostream &os, const RangeImageBorderExtractor::Parameters &p)
std::ostream &	operator<< (std::ostream &os, const Correspondence &c)
	overloaded << operator
template<typename real >
std::ostream &	operator<< (std::ostream &os, const BivariatePolynomialT< real > &p)
std::ostream &	operator<< (std::ostream &os, const NarfKeypoint::Parameters &p)
std::ostream &	operator<< (std::ostream &os, const PointXYZ &p)
std::ostream &	operator<< (std::ostream &os, const PointXYZI &p)
std::ostream &	operator<< (std::ostream &os, const PointXYZL &p)
std::ostream &	operator<< (std::ostream &os, const Label &p)
std::ostream &	operator<< (std::ostream &os, const PointXYZRGBA &p)
std::ostream &	operator<< (std::ostream &os, const PointXYZRGB &p)
std::ostream &	operator<< (std::ostream &os, const PointXYZRGBL &p)
std::ostream &	operator<< (std::ostream &os, const PointXYZHSV &p)
std::ostream &	operator<< (std::ostream &os, const PointXY &p)
std::ostream &	operator<< (std::ostream &os, const InterestPoint &p)
std::ostream &	operator<< (std::ostream &os, const Normal &p)
std::ostream &	operator<< (std::ostream &os, const Axis &p)
std::ostream &	operator<< (std::ostream &os, const _Axis &p)
std::ostream &	operator<< (std::ostream &os, const PointNormal &p)
std::ostream &	operator<< (std::ostream &os, const PointXYZRGBNormal &p)
std::ostream &	operator<< (std::ostream &os, const PointXYZINormal &p)
std::ostream &	operator<< (std::ostream &os, const PointWithRange &p)
std::ostream &	operator<< (std::ostream &os, const RangeImage &r)
std::ostream &	operator<< (std::ostream &os, const PointWithViewpoint &p)
std::ostream &	operator<< (std::ostream &os, const MomentInvariants &p)
std::ostream &	operator<< (std::ostream &os, const PrincipalRadiiRSD &p)
std::ostream &	operator<< (std::ostream &os, const Boundary &p)
std::ostream &	operator<< (std::ostream &os, const PrincipalCurvatures &p)
std::ostream &	operator<< (std::ostream &os, const PFHSignature125 &p)
std::ostream &	operator<< (std::ostream &os, const PFHRGBSignature250 &p)
std::ostream &	operator<< (std::ostream &os, const PPFSignature &p)
std::ostream &	operator<< (std::ostream &os, const PPFRGBSignature &p)
std::ostream &	operator<< (std::ostream &os, const NormalBasedSignature12 &p)
std::ostream &	operator<< (std::ostream &os, const ShapeContext &p)
std::ostream &	operator<< (std::ostream &os, const SHOT &p)
std::ostream &	operator<< (std::ostream &os, const SHOT352 &p)
template<typename PointT >
std::ostream &	operator<< (std::ostream &s, const pcl::PointCloud< PointT > &p)
std::ostream &	operator<< (std::ostream &os, const SHOT1344 &p)
std::ostream &	operator<< (std::ostream &os, const ReferenceFrame &p)
std::ostream &	operator<< (std::ostream &os, const FPFHSignature33 &p)
std::ostream &	operator<< (std::ostream &os, const VFHSignature308 &p)
std::ostream &	operator<< (std::ostream &os, const ESFSignature640 &p)
std::ostream &	operator<< (std::ostream &os, const GFPFHSignature16 &p)
std::ostream &	operator<< (std::ostream &os, const Narf36 &p)
std::ostream &	operator<< (std::ostream &os, const BorderDescription &p)
std::ostream &	operator<< (std::ostream &os, const IntensityGradient &p)
template<int N>
std::ostream &	operator<< (std::ostream &os, const Histogram< N > &p)
std::ostream &	operator<< (std::ostream &os, const PointWithScale &p)
std::ostream &	operator<< (std::ostream &os, const PointSurfel &p)
	PCL_DEPRECATED (inline std::ostream &operator<< (std::ostream &os, const SHOT &p),"SHOT POINT IS DEPRECATED, USE SHOT352 FOR SHAPE AND SHOT1344 FOR SHAPE+COLOR INSTEAD")
template<typename PointInT , typename PointNT , typename PointRFT >
class	PCL_DEPRECATED_CLASS (SHOTEstimationOMP,"SHOTEstimationOMP<..., pcl::SHOT, ...> IS DEPRECATED, USE SHOTEstimationOMP<..., pcl::SHOT352, ...> INSTEAD")< PointInT
template<typename PointNT , typename PointRFT >
class	PCL_DEPRECATED_CLASS (SHOTEstimationOMP,"SHOTEstimationOMP<pcl::PointXYZRGBA,...,pcl::SHOT,...> IS DEPRECATED, USE SHOTEstimationOMP<pcl::PointXYZRGBA,...,pcl::SHOT352,...> FOR SHAPE AND SHOTColorEstimationOMP<pcl::PointXYZRGBA,...,pcl::SHOT1344,...> FOR SHAPE+COLOR INSTEAD")< pcl
template<typename PointInT , typename PointNT , typename PointRFT >
class	PCL_DEPRECATED_CLASS (SHOTEstimation,"SHOTEstimation<..., pcl::SHOT, ...> IS DEPRECATED, USE SHOTEstimation<..., pcl::SHOT352, ...> INSTEAD")< PointInT
	SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals.
template<typename PointNT , typename PointRFT >
class	PCL_DEPRECATED_CLASS (SHOTEstimation,"SHOTEstimation<pcl::PointXYZRGBA,...,pcl::SHOT,...> IS DEPRECATED, USE SHOTEstimation<pcl::PointXYZRGBA,...,pcl::SHOT352,...> FOR SHAPE AND SHOTColorEstimation<pcl::PointXYZRGBA,...,pcl::SHOT1344,...> FOR SHAPE+COLOR INSTEAD")< pcl
	SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals.
template<typename PointNT , typename PointRFT >
class	PCL_DEPRECATED_CLASS (SHOTEstimation,"SHOTEstimation<pcl::PointXYZRGBA,...,Eigen::MatrixXf,...> IS DEPRECATED, USE SHOTColorEstimation<pcl::PointXYZRGBA,...,Eigen::MatrixXf,...> FOR SHAPE AND SHAPE+COLOR INSTEAD")< pcl
	SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals.
template<typename FloatVectorT >
float	PF_Norm (FloatVectorT A, FloatVectorT B, int dim, float P1, float P2)
	Compute the PF norm of the vector between two points.
PCL_EXPORTS bool	planeWithPlaneIntersection (const Eigen::Vector4f &plane_a, const Eigen::Vector4f &fplane_b, Eigen::VectorXf &line, double angular_tolerance=0.1)
	Determine the line of intersection of two non-parallel planes using lagrange multipliers in: "Intersection of Two Planes, John Krumm, Microsoft Research, Redmond, WA, USA".
void	PointCloudXYZRGBtoXYZHSV (PointCloud< PointXYZRGB > &in, PointCloud< PointXYZHSV > &out)
	Convert a XYZRGB point cloud to a XYZHSV.
void	PointCloudXYZRGBtoXYZI (PointCloud< PointXYZRGB > &in, PointCloud< PointXYZI > &out)
	Convert a XYZRGB point cloud to a XYZI.
template<typename Point >
double	pointToPlaneDistance (const Point &p, double a, double b, double c, double d)
	Get the distance from a point to a plane (unsigned) defined by ax+by+cz+d=0.
template<typename Point >
double	pointToPlaneDistance (const Point &p, const Eigen::Vector4f &plane_coefficients)
	Get the distance from a point to a plane (unsigned) defined by ax+by+cz+d=0.
template<typename Point >
double	pointToPlaneDistanceSigned (const Point &p, double a, double b, double c, double d)
	Get the distance from a point to a plane (signed) defined by ax+by+cz+d=0.
template<typename Point >
double	pointToPlaneDistanceSigned (const Point &p, const Eigen::Vector4f &plane_coefficients)
	Get the distance from a point to a plane (signed) defined by ax+by+cz+d=0.
void	PointXYZHSVtoXYZRGB (PointXYZHSV &in, PointXYZRGB &out)
	Convert a XYZHSV point type to a XYZRGB.
void	PointXYZRGBtoXYZHSV (PointXYZRGB &in, PointXYZHSV &out)
	Convert a XYZRGB point type to a XYZHSV.
void	PointXYZRGBtoXYZI (PointXYZRGB &in, PointXYZI &out)
	Convert a XYZRGB point type to a XYZI.
template<typename Point >
void	projectPoint (const Point &p, const Eigen::Vector4f &model_coefficients, Point &q)
	Project a point on a planar model given by a set of normalized coefficients.
float	rad2deg (float alpha)
	Convert an angle from radians to degrees.
double	rad2deg (double alpha)
	Convert an angle from radians to degrees.
template<typename PointT >
void	removeNaNFromPointCloud (const pcl::PointCloud< PointT > &cloud_in, std::vector< int > &index)
	Removes points with x, y, or z equal to NaN.
template<typename PointT >
void	removeNaNFromPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, std::vector< int > &index)
	Removes points with x, y, or z equal to NaN.
static const std::map < pcl::SacModel, unsigned int >	SAC_SAMPLE_SIZE (sample_size_pairs, sample_size_pairs+sizeof(sample_size_pairs)/sizeof(SampleSizeModel))
template<typename Derived >
void	saveBinary (const Eigen::MatrixBase< Derived > &matrix, std::ostream &file)
	Write a matrix to an output stream.
template<typename FloatVectorT >
float	selectNorm (FloatVectorT A, FloatVectorT B, int dim, NormType norm_type)
	Method that calculates any norm type available, based on the norm_type variable.
void	solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix, const Eigen::Vector4f &point, Eigen::Vector4f &plane_parameters, float &curvature)
	Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.
void	solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix, float &nx, float &ny, float &nz, float &curvature)
	Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.
double	sqrPointToLineDistance (const Eigen::Vector4f &pt, const Eigen::Vector4f &line_pt, const Eigen::Vector4f &line_dir)
	Get the square distance from a point to a line (represented by a point and a direction)
double	sqrPointToLineDistance (const Eigen::Vector4f &pt, const Eigen::Vector4f &line_pt, const Eigen::Vector4f &line_dir, const double sqr_length)
	Get the square distance from a point to a line (represented by a point and a direction)
template<typename Scalar >
Scalar	sqrt (const Scalar &val)
template<>
double	sqrt< double > (const double &val)
template<>
float	sqrt< float > (const float &val)
template<>
long double	sqrt< long double > (const long double &val)
template<typename PointType1 , typename PointType2 >
float	squaredEuclideanDistance (const PointType1 &p1, const PointType2 &p2)
	Calculate the squared euclidean distance between the two given points.
template<typename FloatVectorT >
float	Sublinear_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the sublinear norm of the vector between two points.
void	throwPCLIOException (const char *function_name, const char *file_name, unsigned line_number, const char *format,...)
template<typename PointT >
void	toROSMsg (const pcl::PointCloud< PointT > &cloud, sensor_msgs::PointCloud2 &msg)
	Convert a pcl::PointCloud<T> object to a PointCloud2 binary data blob.
template<typename CloudT >
void	toROSMsg (const CloudT &cloud, sensor_msgs::Image &msg)
	Copy the RGB fields of a PointCloud into sensor_msgs::Image format.
void	toROSMsg (const sensor_msgs::PointCloud2 &cloud, sensor_msgs::Image &msg)
	Copy the RGB fields of a PointCloud2 msg into sensor_msgs::Image format.
template<typename PointT >
PointT	transformPoint (const PointT &point, const Eigen::Affine3f &transform)
	Transform a point with members x,y,z.
template<typename PointT >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Affine3f &transform)
	Apply an affine transform defined by an Eigen Transform.
template<typename PointT >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Affine3f &transform)
	Apply an affine transform defined by an Eigen Transform.
template<typename PointT >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix4f &transform)
	Apply an affine transform defined by an Eigen Transform.
template<typename PointT >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Vector3f &offset, const Eigen::Quaternionf &rotation)
	Apply a rigid transform defined by a 3D offset and a quaternion.
template<typename PointT >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Affine3f &transform)
	Transform a point cloud and rotate its normals using an Eigen transform.
template<typename PointT >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix4f &transform)
	Transform a point cloud and rotate its normals using an Eigen transform.
template<typename PointT >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Vector3f &offset, const Eigen::Quaternionf &rotation)
	Transform a point cloud and rotate its normals using an Eigen transform.
Variables
class< SHOT >	__pad0__
const unsigned int	edgeTable [256]
struct pcl::_PointXYZHSV	EIGEN_ALIGN16
const int	I_SHIFT_EDGE [3][2]
const int	I_SHIFT_EP [12][2]
	The 12 edges of a cell.
const int	I_SHIFT_PT [4]
static const int	SAC_LMEDS = 1
static const int	SAC_MLESAC = 5
static const int	SAC_MSAC = 2
static const int	SAC_PROSAC = 6
static const int	SAC_RANSAC = 0
static const int	SAC_RMSAC = 4
static const int	SAC_RRANSAC = 3
const int	triTable [256][16]

---

Detailed Description

Software License Agreement (BSD License)

Copyright (c) 2011, Willow Garage, Inc. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Willow Garage, Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Id:

cloud_viewer.cpp 6161 2012-07-05 17:37:29Z rusu

---

Typedef Documentation

typedef Eigen::Map<Eigen::Array3f> pcl::Array3fMap

Definition at line 174 of file point_types.hpp.

typedef const Eigen::Map<const Eigen::Array3f> pcl::Array3fMapConst

Definition at line 175 of file point_types.hpp.

typedef Eigen::Map<Eigen::Array4f, Eigen::Aligned> pcl::Array4fMap

Definition at line 176 of file point_types.hpp.

typedef const Eigen::Map<const Eigen::Array4f, Eigen::Aligned> pcl::Array4fMapConst

Definition at line 177 of file point_types.hpp.

typedef BivariatePolynomialT<float> pcl::BivariatePolynomial

Definition at line 137 of file bivariate_polynomial.h.

typedef BivariatePolynomialT<double> pcl::BivariatePolynomiald

Definition at line 136 of file bivariate_polynomial.h.

typedef std::bitset<32> pcl::BorderTraits

Data type to store extended information about a transition from foreground to backgroundSpecification of the fields for BorderDescription::traits.

Definition at line 255 of file point_types.h.

typedef pcl::PointCloud<pcl::PointXYZRGB> pcl::cc

Definition at line 98 of file visualization/src/cloud_viewer.cpp.

typedef pcl::PointCloud<pcl::PointXYZRGBA> pcl::cca

Definition at line 97 of file visualization/src/cloud_viewer.cpp.

typedef std::vector< pcl::Correspondence, Eigen::aligned_allocator<pcl::Correspondence> > pcl::Correspondences

Definition at line 92 of file correspondence.h.

typedef boost::shared_ptr<const Correspondences > pcl::CorrespondencesConstPtr

Definition at line 94 of file correspondence.h.

typedef boost::shared_ptr<Correspondences> pcl::CorrespondencesPtr

Definition at line 93 of file correspondence.h.

typedef pcl::PointCloud<pcl::PointXYZI> pcl::gc

Definition at line 99 of file visualization/src/cloud_viewer.cpp.

typedef boost::shared_ptr<const std::vector<int> > pcl::IndicesConstPtr

Definition at line 64 of file pcl_base.h.

typedef boost::shared_ptr<std::vector<int> > pcl::IndicesPtr

Definition at line 63 of file pcl_base.h.

typedef pcl::PointCloud<pcl::PointXYZ> pcl::mc

Definition at line 100 of file visualization/src/cloud_viewer.cpp.

typedef std::vector<detail::FieldMapping> pcl::MsgFieldMap

Definition at line 69 of file point_cloud.h.

typedef std::vector<PointCorrespondence3D, Eigen::aligned_allocator<PointCorrespondence3D> > pcl::PointCorrespondences3DVector

Definition at line 133 of file correspondence.h.

typedef std::vector<PointCorrespondence6D, Eigen::aligned_allocator<PointCorrespondence6D> > pcl::PointCorrespondences6DVector

Definition at line 149 of file correspondence.h.

typedef PolynomialCalculationsT<float> pcl::PolynomialCalculations

Definition at line 129 of file polynomial_calculations.h.

typedef PolynomialCalculationsT<double> pcl::PolynomialCalculationsd

Definition at line 128 of file polynomial_calculations.h.

typedef boost::shared_ptr<pcl::TextureMesh const> pcl::TextureMeshConstPtr

Definition at line 112 of file TextureMesh.h.

typedef boost::shared_ptr<pcl::TextureMesh> pcl::TextureMeshPtr

Definition at line 111 of file TextureMesh.h.

typedef Eigen::Map<Eigen::Vector3f> pcl::Vector3fMap

Definition at line 178 of file point_types.hpp.

typedef const Eigen::Map<const Eigen::Vector3f> pcl::Vector3fMapConst

Definition at line 179 of file point_types.hpp.

typedef Eigen::Map<Eigen::Vector4f, Eigen::Aligned> pcl::Vector4fMap

Definition at line 180 of file point_types.hpp.

typedef const Eigen::Map<const Eigen::Vector4f, Eigen::Aligned> pcl::Vector4fMapConst

Definition at line 181 of file point_types.hpp.

typedef VectorAverage<float, 2> pcl::VectorAverage2f

Definition at line 110 of file vector_average.h.

typedef VectorAverage<float, 3> pcl::VectorAverage3f

Definition at line 111 of file vector_average.h.

typedef VectorAverage<float, 4> pcl::VectorAverage4f

Definition at line 112 of file vector_average.h.

---

Enumeration Type Documentation

enum pcl::BorderTrait

Specification of the fields for BorderDescription::traits.

Enumerator:

BORDER_TRAIT__OBSTACLE_BORDER
BORDER_TRAIT__SHADOW_BORDER
BORDER_TRAIT__VEIL_POINT
BORDER_TRAIT__SHADOW_BORDER_TOP
BORDER_TRAIT__SHADOW_BORDER_RIGHT
BORDER_TRAIT__SHADOW_BORDER_BOTTOM
BORDER_TRAIT__SHADOW_BORDER_LEFT
BORDER_TRAIT__OBSTACLE_BORDER_TOP
BORDER_TRAIT__OBSTACLE_BORDER_RIGHT
BORDER_TRAIT__OBSTACLE_BORDER_BOTTOM
BORDER_TRAIT__OBSTACLE_BORDER_LEFT
BORDER_TRAIT__VEIL_POINT_TOP
BORDER_TRAIT__VEIL_POINT_RIGHT
BORDER_TRAIT__VEIL_POINT_BOTTOM
BORDER_TRAIT__VEIL_POINT_LEFT

Definition at line 265 of file point_types.h.

enum pcl::NormType

Enum that defines all the types of norms available.

Note:

Any new norm type should have its own enum value and its own case in the selectNorm () method

Enumerator:

L1
L2_SQR
L2
LINF
JM
B
SUBLINEAR
CS
DIV
PF
K
KL
HIK

Definition at line 52 of file norms.h.

enum pcl::SacModel

Enumerator:

SACMODEL_PLANE
SACMODEL_LINE
SACMODEL_CIRCLE2D
SACMODEL_CIRCLE3D
SACMODEL_SPHERE
SACMODEL_CYLINDER
SACMODEL_CONE
SACMODEL_TORUS
SACMODEL_PARALLEL_LINE
SACMODEL_PERPENDICULAR_PLANE
SACMODEL_PARALLEL_LINES
SACMODEL_NORMAL_PLANE
SACMODEL_NORMAL_SPHERE
SACMODEL_REGISTRATION
SACMODEL_PARALLEL_PLANE
SACMODEL_NORMAL_PARALLEL_PLANE
SACMODEL_STICK

Definition at line 45 of file model_types.h.

---

Function Documentation

template<typename PointT >

void pcl::approximatePolygon	(	const PlanarPolygon< PointT > &	polygon,
		PlanarPolygon< PointT > &	approx_polygon,
		float	threshold,
		bool	refine = false,
		bool	closed = true
	)

see approximatePolygon2D

Author:

Suat Gedikli <gedikli@willowgarage.com>

Definition at line 43 of file polygon_operations.hpp.

template<typename PointT >

void pcl::approximatePolygon2D	(	const typename PointCloud< PointT >::VectorType &	polygon,
		typename PointCloud< PointT >::VectorType &	approx_polygon,
		float	threshold,
		bool	refine = false,
		bool	closed = true
	)

returns an approximate polygon to given 2D contour. Uses just X and Y values.

Note:

if refinement is not turned on, the resulting polygon will contain points from the original contour with their original z values (if any)

if refinement is turned on, the z values of the refined polygon are not valid and should be set to 0 if point contains z attribute.

Parameters:

[in]	polygon	input polygon
[out]	approx_polygon	approximate polygon
[in]	threshold	maximum allowed distance of an input vertex to an output edge
[in]	closed	whether it is a closed polygon or a polyline

Author:

Suat Gedikli <gedikli@willowgarage.com>

Definition at line 71 of file polygon_operations.hpp.

template<typename FloatVectorT >

float pcl::B_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the B norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 132 of file norms.hpp.

bool pcl::compareLabeledPointClusters	(	const pcl::PointIndices &	a,
		const pcl::PointIndices &	b
	)		[inline]

Sort clusters method (for std::sort).

Definition at line 183 of file extract_labeled_clusters.h.

bool pcl::comparePointClusters	(	const pcl::PointIndices &	a,
		const pcl::PointIndices &	b
	)		[inline]

Sort clusters method (for std::sort).

Definition at line 418 of file extract_clusters.h.

template<typename PointT >

unsigned int pcl::compute3DCentroid	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Vector4f &	centroid
	)		[inline]

Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.

Parameters:

[in]	cloud	the input point cloud
[out]	centroid	the output centroid

Returns:

number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.

Note:

if return value is 0, the centroid is not changed, thus not valid.

Definition at line 46 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::compute3DCentroid	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		Eigen::Vector4f &	centroid
	)		[inline]

Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[out]	centroid	the output centroid

Returns:

number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.

Note:

if return value is 0, the centroid is not changed, thus not valid.

Definition at line 86 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::compute3DCentroid	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		Eigen::Vector4f &	centroid
	)		[inline]

Compute the 3D (X-Y-Z) centroid of a set of points using their indices and return it as a 3D vector.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[out]	centroid	the output centroid

Returns:

number of valid point used to determine the centroid. In case of dense point clouds, this is the same as the size of input cloud.

Note:

if return value is 0, the centroid is not changed, thus not valid.

Definition at line 124 of file centroid.hpp.

template<typename Matrix , typename Vector >

void pcl::computeCorrespondingEigenVector	(	const Matrix &	mat,
		const typename Matrix::Scalar &	eigenvalue,
		Vector &	eigenvector
	)		[inline]

determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi definite input matrix

Parameters:

[in]	mat	symmetric positive semi definite input matrix
[in]	eigenvalue	the eigenvalue which corresponding eigenvector is to be computed
[out]	eigenvector	the corresponding eigenvector for the input eigenvalue

Definition at line 316 of file eigen.h.

template<typename PointT >

unsigned pcl::computeCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const Eigen::Vector4f &	centroid,
		Eigen::Matrix3f &	covariance_matrix
	)		[inline]

Compute the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.

Parameters:

[in]	cloud	the input point cloud
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Note:

if return value is 0, the covariance matrix is not changed, thus not valid.

Definition at line 132 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		const Eigen::Vector4f &	centroid,
		Eigen::Matrix3f &	covariance_matrix
	)		[inline]

Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 209 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		const Eigen::Vector4f &	centroid,
		Eigen::Matrix3f &	covariance_matrix
	)		[inline]

Compute the 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Note: the covariance matrix is not normalized with the number of points. For a normalized covariance, please use computeNormalizedCovarianceMatrix.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 274 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Matrix3f &	covariance_matrix
	)		[inline]

Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note:

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters:

[in]	cloud	the input point cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 312 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		Eigen::Matrix3f &	covariance_matrix
	)		[inline]

Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note:

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 366 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		Eigen::Matrix3f &	covariance_matrix
	)		[inline]

Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note:

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 418 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Matrix3d &	covariance_matrix
	)		[inline]

Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Parameters:

[in]	cloud	the input point cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 427 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		Eigen::Matrix3d &	covariance_matrix
	)		[inline]

Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 481 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		Eigen::Matrix3d &	covariance_matrix
	)		[inline]

Compute the normalized 3x3 covariance matrix for a already demeaned point cloud. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 534 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrixNormalized	(	const pcl::PointCloud< PointT > &	cloud,
		const Eigen::Vector4f &	centroid,
		Eigen::Matrix3f &	covariance_matrix
	)		[inline]

Compute normalized the 3x3 covariance matrix of a given set of points. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of points in the point cloud. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.

Parameters:

[in]	cloud	the input point cloud
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 197 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrixNormalized	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		const Eigen::Vector4f &	centroid,
		Eigen::Matrix3f &	covariance_matrix
	)		[inline]

Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 284 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeCovarianceMatrixNormalized	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		const Eigen::Vector4f &	centroid,
		Eigen::Matrix3f &	covariance_matrix
	)		[inline]

Compute the normalized 3x3 covariance matrix of a given set of points using their indices. The result is returned as a Eigen::Matrix3f. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, use computeCovarianceMatrix and scale the covariance matrix with 1 / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by the computeCovarianceMatrix function.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	the point cloud indices that need to be used
[in]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 298 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeMeanAndCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Matrix3f &	covariance_matrix,
		Eigen::Vector4f &	centroid
	)		[inline]

Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note:

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters:

[in]	cloud	the input point cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix
[out]	centroid	the centroid of the set of points in the cloud

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 543 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeMeanAndCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		Eigen::Matrix3f &	covariance_matrix,
		Eigen::Vector4f &	centroid
	)		[inline]

Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note:

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	covariance_matrix	the resultant 3x3 covariance matrix
[out]	centroid	the centroid of the set of points in the cloud

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 608 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeMeanAndCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		Eigen::Matrix3f &	covariance_matrix,
		Eigen::Vector4f &	centroid
	)		[inline]

Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Note:

This method is theoretically exact. However using float for internal calculations reduces the accuracy but increases the efficiency.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 675 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeMeanAndCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Matrix3d &	covariance_matrix,
		Eigen::Vector4d &	centroid
	)		[inline]

Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Parameters:

[in]	cloud	the input point cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix
[out]	centroid	the centroid of the set of points in the cloud

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 685 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeMeanAndCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		Eigen::Matrix3d &	covariance_matrix,
		Eigen::Vector4d &	centroid
	)		[inline]

Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	covariance_matrix	the resultant 3x3 covariance matrix
[out]	centroid	the centroid of the set of points in the cloud

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 750 of file centroid.hpp.

template<typename PointT >

unsigned int pcl::computeMeanAndCovarianceMatrix	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		Eigen::Matrix3d &	covariance_matrix,
		Eigen::Vector4d &	centroid
	)		[inline]

Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single loop. Normalized means that every entry has been divided by the number of entries in indices. For small number of points, or if you want explicitely the sample-variance, scale the covariance matrix with n / (n-1), where n is the number of points used to calculate the covariance matrix and is returned by this function.

Parameters:

[in]	cloud	the input point cloud
[in]	indices	subset of points given by their indices
[out]	centroid	the centroid of the set of points in the cloud
[out]	covariance_matrix	the resultant 3x3 covariance matrix

Returns:

number of valid point used to determine the covariance matrix. In case of dense point clouds, this is the same as the size of input cloud.

Definition at line 817 of file centroid.hpp.

template<typename PointT >

void pcl::computeNDCentroid	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::VectorXf &	centroid
	)		[inline]

General, all purpose nD centroid estimation for a set of points using their indices.

Parameters:

cloud	the input point cloud
centroid	the output centroid

Definition at line 913 of file centroid.hpp.

template<typename PointT >

void pcl::computeNDCentroid	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		Eigen::VectorXf &	centroid
	)		[inline]

General, all purpose nD centroid estimation for a set of points using their indices.

Parameters:

cloud	the input point cloud
indices	the point cloud indices that need to be used
centroid	the output centroid

Definition at line 934 of file centroid.hpp.

template<typename PointT >

void pcl::computeNDCentroid	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		Eigen::VectorXf &	centroid
	)		[inline]

General, all purpose nD centroid estimation for a set of points using their indices.

Parameters:

cloud	the input point cloud
indices	the point cloud indices that need to be used
centroid	the output centroid

Definition at line 956 of file centroid.hpp.

bool pcl::computePairFeatures	(	const Eigen::Vector4f &	p1,
		const Eigen::Vector4f &	n1,
		const Eigen::Vector4f &	p2,
		const Eigen::Vector4f &	n2,
		float &	f1,
		float &	f2,
		float &	f3,
		float &	f4
	)

Compute the 4-tuple representation containing the three angles and one distance between two points represented by Cartesian coordinates and normals.

Note:

For explanations about the features, please see the literature mentioned above (the order of the features might be different).

Parameters:

[in]	p1	the first XYZ point
[in]	n1	the first surface normal
[in]	p2	the second XYZ point
[in]	n2	the second surface normal
[out]	f1	the first angular feature (angle between the projection of nq_idx and u)
[out]	f2	the second angular feature (angle between nq_idx and v)
[out]	f3	the third angular feature (angle between np_idx and |p_idx - q_idx|)
[out]	f4	the distance feature (p_idx - q_idx)

Note:

For efficiency reasons, we assume that the point data passed to the method is finite.

Definition at line 47 of file pfh.cpp.

template<typename PointT >

void pcl::computePointNormal	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Vector4f &	plane_parameters,
		float &	curvature
	)		[inline]

Compute the Least-Squares plane fit for a given set of points, and return the estimated plane parameters together with the surface curvature.

Parameters:

cloud	the input point cloud
plane_parameters	the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
curvature	the estimated surface curvature as a measure of

Definition at line 58 of file normal_3d.h.

template<typename PointT >

void pcl::computePointNormal	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		Eigen::Vector4f &	plane_parameters,
		float &	curvature
	)		[inline]

Compute the Least-Squares plane fit for a given set of points, using their indices, and return the estimated plane parameters together with the surface curvature.

Parameters:

cloud	the input point cloud
indices	the point cloud indices that need to be used
plane_parameters	the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
curvature	the estimated surface curvature as a measure of

Definition at line 89 of file normal_3d.h.

bool pcl::computePPFPairFeature	(	const Eigen::Vector4f &	p1,
		const Eigen::Vector4f &	n1,
		const Eigen::Vector4f &	p2,
		const Eigen::Vector4f &	n2,
		float &	f1,
		float &	f2,
		float &	f3,
		float &	f4
	)

Parameters:

[in]	p1
[in]	n1
[in]	p2
[in]	n2
[out]	f1
[out]	f2
[out]	f3
[out]	f4

Definition at line 45 of file ppf.cpp.

bool pcl::computeRGBPairFeatures	(	const Eigen::Vector4f &	p1,
		const Eigen::Vector4f &	n1,
		const Eigen::Vector4i &	colors1,
		const Eigen::Vector4f &	p2,
		const Eigen::Vector4f &	n2,
		const Eigen::Vector4i &	colors2,
		float &	f1,
		float &	f2,
		float &	f3,
		float &	f4,
		float &	f5,
		float &	f6,
		float &	f7
	)

Definition at line 45 of file pfhrgb.cpp.

template<typename Matrix , typename Roots >

void pcl::computeRoots	(	const Matrix &	m,
		Roots &	roots
	)		[inline]

computes the roots of the characteristic polynomial of the input matrix m, which are the eigenvalues

Parameters:

[in]	m	input matrix
[out]	roots	roots of the characteristic polynomial of the input matrix m, which are the eigenvalues

Definition at line 142 of file eigen.h.

template<typename Scalar , typename Roots >

void pcl::computeRoots2	(	const Scalar &	b,
		const Scalar &	c,
		Roots &	roots
	)		[inline]

Compute the roots of a quadratic polynom x^2 + b*x + c = 0.

Parameters:

[in]	b	linear parameter
[in]	c	constant parameter
[out]	roots	solutions of x^2 + b*x + c = 0

Definition at line 124 of file eigen.h.

template<typename PointInT , typename PointNT , typename PointOutT >

Eigen::MatrixXf pcl::computeRSD	(	boost::shared_ptr< const pcl::PointCloud< PointInT > > &	surface,
		boost::shared_ptr< const pcl::PointCloud< PointNT > > &	normals,
		const std::vector< int > &	indices,
		double	max_dist,
		int	nr_subdiv,
		double	plane_radius,
		PointOutT &	radii,
		bool	compute_histogram = false
	)

Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals.

Parameters:

[in]	surface	the dataset containing the XYZ points
[in]	normals	the dataset containing the surface normals at each point in the dataset
[in]	indices	the neighborhood point indices in the dataset (first point is used as the reference)
[in]	max_dist	the upper bound for the considered distance interval
[in]	nr_subdiv	the number of subdivisions for the considered distance interval
[in]	plane_radius	maximum radius, above which everything can be considered planar
[in]	radii	the output point of a type that should have r_min and r_max fields
[in]	compute_histogram	if not false, the full neighborhood histogram is provided, usable as a point signature

Note:

: orientation is neglected!

: we neglect points that are outside the specified interval!

Definition at line 46 of file rsd.hpp.

template<typename PointNT , typename PointOutT >

Eigen::MatrixXf pcl::computeRSD	(	boost::shared_ptr< const pcl::PointCloud< PointNT > > &	normals,
		const std::vector< int > &	indices,
		const std::vector< float > &	sqr_dists,
		double	max_dist,
		int	nr_subdiv,
		double	plane_radius,
		PointOutT &	radii,
		bool	compute_histogram = false
	)

Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals.

Parameters:

[in]	normals	the dataset containing the surface normals at each point in the dataset
[in]	indices	the neighborhood point indices in the dataset (first point is used as the reference)
[in]	sqr_dists	the squared distances from the first to all points in the neighborhood
[in]	max_dist	the upper bound for the considered distance interval
[in]	nr_subdiv	the number of subdivisions for the considered distance interval
[in]	plane_radius	maximum radius, above which everything can be considered planar
[in]	radii	the output point of a type that should have r_min and r_max fields
[in]	compute_histogram	if not false, the full neighborhood histogram is provided, usable as a point signature

Note:

: orientation is neglected!

: we neglect points that are outside the specified interval!

Definition at line 147 of file rsd.hpp.

template<typename PointT >

pcl::PointCloud<pcl::VFHSignature308>::Ptr pcl::computeVFH	(	typename PointCloud< PointT >::ConstPtr	cloud,
		double	radius
	)

Helper function to extract the VFH feature describing the given point cloud.

Parameters:

points	point cloud for feature extraction
radius	search radius for normal estimation

Returns:

point cloud containing the extracted feature

Definition at line 57 of file vfh_nn_classifier.h.

template<typename PointIn1T , typename PointIn2T , typename PointOutT >

void pcl::concatenateFields	(	const pcl::PointCloud< PointIn1T > &	cloud1_in,
		const pcl::PointCloud< PointIn2T > &	cloud2_in,
		pcl::PointCloud< PointOutT > &	cloud_out
	)

Concatenate two datasets representing different fields.

Note:

If the input datasets have overlapping fields (i.e., both contain the same fields), then the data in the second cloud (cloud2_in) will overwrite the data in the first (cloud1_in).

Parameters:

[in]	cloud1_in	the first input dataset
[in]	cloud2_in	the second input dataset (overwrites the fields of the first dataset for those that are shared)
[out]	cloud_out	the resultant output dataset created by the concatenation of all the fields in the input datasets

Definition at line 364 of file common/include/pcl/common/impl/io.hpp.

bool pcl::concatenateFields	(	const sensor_msgs::PointCloud2 &	cloud1_in,
		const sensor_msgs::PointCloud2 &	cloud2_in,
		sensor_msgs::PointCloud2 &	cloud_out
	)

Concatenate two datasets representing different fields.

Note:

If the input datasets have overlapping fields (i.e., both contain the same fields), then the data in the second cloud (cloud2_in) will overwrite the data in the first (cloud1_in).

Parameters:

[in]	cloud1_in	the first input dataset
[in]	cloud2_in	the second input dataset (overwrites the fields of the first dataset for those that are shared)
[out]	cloud_out	the output dataset created by concatenating all the fields in the input datasets

Definition at line 69 of file common/src/io.cpp.

bool pcl::concatenatePointCloud	(	const sensor_msgs::PointCloud2 &	cloud1,
		const sensor_msgs::PointCloud2 &	cloud2,
		sensor_msgs::PointCloud2 &	cloud_out
	)

Concatenate two sensor_msgs::PointCloud2.

Parameters:

[in]	cloud1	the first input point cloud dataset
[in]	cloud2	the second input point cloud dataset
[out]	cloud_out	the resultant output point cloud dataset

Returns:

true if successful, false if failed (e.g., name/number of fields differs)

Definition at line 218 of file common/src/io.cpp.

template<typename PointInT , typename PointOutT >

void pcl::copyPointCloud	(	const pcl::PointCloud< PointInT > &	cloud_in,
		pcl::PointCloud< PointOutT > &	cloud_out
	)

Copy all the fields from a given point cloud into a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 108 of file common/include/pcl/common/impl/io.hpp.

void pcl::copyPointCloud	(	const sensor_msgs::PointCloud2 &	cloud_in,
		const std::vector< int > &	indices,
		sensor_msgs::PointCloud2 &	cloud_out
	)

Extract the indices of a given point cloud as a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 343 of file common/src/io.cpp.

void pcl::copyPointCloud	(	const sensor_msgs::PointCloud2 &	cloud_in,
		sensor_msgs::PointCloud2 &	cloud_out
	)

Copy fields and point cloud data from cloud_in to cloud_out.

Parameters:

[in]	cloud_in	the input point cloud dataset
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 374 of file common/src/io.cpp.

template<typename PointT >

void pcl::copyPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const std::vector< int > &	indices,
		pcl::PointCloud< PointT > &	cloud_out
	)

Extract the indices of a given point cloud as a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 130 of file common/include/pcl/common/impl/io.hpp.

template<typename PointT >

void pcl::copyPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const std::vector< int, Eigen::aligned_allocator< int > > &	indices,
		pcl::PointCloud< PointT > &	cloud_out
	)

Extract the indices of a given point cloud as a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 155 of file common/include/pcl/common/impl/io.hpp.

template<typename PointInT , typename PointOutT >

void pcl::copyPointCloud	(	const pcl::PointCloud< PointInT > &	cloud_in,
		const std::vector< int > &	indices,
		pcl::PointCloud< PointOutT > &	cloud_out
	)

Extract the indices of a given point cloud as a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 181 of file common/include/pcl/common/impl/io.hpp.

template<typename PointInT , typename PointOutT >

void pcl::copyPointCloud	(	const pcl::PointCloud< PointInT > &	cloud_in,
		const std::vector< int, Eigen::aligned_allocator< int > > &	indices,
		pcl::PointCloud< PointOutT > &	cloud_out
	)

Extract the indices of a given point cloud as a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 208 of file common/include/pcl/common/impl/io.hpp.

template<typename PointT >

void pcl::copyPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const PointIndices &	indices,
		pcl::PointCloud< PointT > &	cloud_out
	)

Extract the indices of a given point cloud as a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	indices	the PointIndices structure representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

template<typename PointInT , typename PointOutT >

void pcl::copyPointCloud	(	const pcl::PointCloud< PointInT > &	cloud_in,
		const PointIndices &	indices,
		pcl::PointCloud< PointOutT > &	cloud_out
	)

Extract the indices of a given point cloud as a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	indices	the PointIndices structure representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

template<typename PointT >

void pcl::copyPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const std::vector< pcl::PointIndices > &	indices,
		pcl::PointCloud< PointT > &	cloud_out
	)

Extract the indices of a given point cloud as a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 288 of file common/include/pcl/common/impl/io.hpp.

template<typename PointInT , typename PointOutT >

void pcl::copyPointCloud	(	const pcl::PointCloud< PointInT > &	cloud_in,
		const std::vector< pcl::PointIndices > &	indices,
		pcl::PointCloud< PointOutT > &	cloud_out
	)

Extract the indices of a given point cloud as a new point cloud.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	indices	the vector of indices representing the points to be copied from cloud_in
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 325 of file common/include/pcl/common/impl/io.hpp.

template<typename Type >

void pcl::copyStringValue	(	const std::string &	st,
		sensor_msgs::PointCloud2 &	cloud,
		unsigned int	point_index,
		unsigned int	field_idx,
		unsigned int	fields_count
	)		[inline]

Copy one single value of type T (uchar, char, uint, int, float, double, ...) from a string.

Uses aoti/atof to do the conversion. Checks if the st is "nan" and converts it accordingly.

Parameters:

[in]	st	the string containing the value to convert and copy
[out]	cloud	the cloud to copy it to
[in]	point_index	the index of the point
[in]	field_idx	the index of the dimension/field
[in]	fields_count	the current fields count

Definition at line 317 of file io/include/pcl/io/file_io.h.

template<>

void pcl::copyStringValue< int8_t >	(	const std::string &	st,
		sensor_msgs::PointCloud2 &	cloud,
		unsigned int	point_index,
		unsigned int	field_idx,
		unsigned int	fields_count
	)		[inline]

Definition at line 339 of file io/include/pcl/io/file_io.h.

template<>

void pcl::copyStringValue< uint8_t >	(	const std::string &	st,
		sensor_msgs::PointCloud2 &	cloud,
		unsigned int	point_index,
		unsigned int	field_idx,
		unsigned int	fields_count
	)		[inline]

Definition at line 363 of file io/include/pcl/io/file_io.h.

virtual void pcl::copyToFloatArray	(	const SHOT &	p,
		float *	out
	)		const [virtual]

Definition at line 455 of file point_representation.h.

template<typename Type >

void pcl::copyValueString	(	const sensor_msgs::PointCloud2 &	cloud,
		const unsigned int	point_index,
		const int	point_size,
		const unsigned int	field_idx,
		const unsigned int	fields_count,
		std::ostream &	stream
	)		[inline]

insers a value of type Type (uchar, char, uint, int, float, double, ...) into a stringstream.

If the value is NaN, it inserst "nan".

Parameters:

[in]	cloud	the cloud to copy from
[in]	point_index	the index of the point
[in]	point_size	the size of the point in the cloud
[in]	field_idx	the index of the dimension/field
[in]	fields_count	the current fields count
[out]	stream	the ostringstream to copy into

Definition at line 234 of file io/include/pcl/io/file_io.h.

template<>

void pcl::copyValueString< int8_t >	(	const sensor_msgs::PointCloud2 &	cloud,
		const unsigned int	point_index,
		const int	point_size,
		const unsigned int	field_idx,
		const unsigned int	fields_count,
		std::ostream &	stream
	)		[inline]

Definition at line 249 of file io/include/pcl/io/file_io.h.

template<>

void pcl::copyValueString< uint8_t >	(	const sensor_msgs::PointCloud2 &	cloud,
		const unsigned int	point_index,
		const int	point_size,
		const unsigned int	field_idx,
		const unsigned int	fields_count,
		std::ostream &	stream
	)		[inline]

Definition at line 265 of file io/include/pcl/io/file_io.h.

template<typename PointT >

void pcl::createMapping	(	const std::vector< sensor_msgs::PointField > &	msg_fields,
		MsgFieldMap &	field_map
	)

Todo:

One could construct a pathological case where the struct has a field where the serialized data has padding

Definition at line 119 of file conversions.h.

template<typename FloatVectorT >

float pcl::CS_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the CS norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 161 of file norms.hpp.

float pcl::deg2rad

(

float

alpha

)

[inline]

Convert an angle from degrees to radians.

Parameters:

alpha

the input angle (in degrees)

Definition at line 61 of file angles.hpp.

double pcl::deg2rad

(

double

alpha

)

[inline]

Convert an angle from degrees to radians.

Parameters:

alpha

the input angle (in degrees)

Definition at line 73 of file angles.hpp.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const Eigen::Vector4f &	centroid,
		pcl::PointCloud< PointT > &	cloud_out
	)

Subtract a centroid from a point cloud and return the de-meaned representation.

Parameters:

cloud_in	the input point cloud
centroid	the centroid of the point cloud
cloud_out	the resultant output point cloud

Definition at line 826 of file centroid.hpp.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const std::vector< int > &	indices,
		const Eigen::Vector4f &	centroid,
		pcl::PointCloud< PointT > &	cloud_out
	)

Subtract a centroid from a point cloud and return the de-meaned representation.

Parameters:

cloud_in	the input point cloud
indices	the set of point indices to use from the input point cloud
centroid	the centroid of the point cloud
cloud_out	the resultant output point cloud

Definition at line 839 of file centroid.hpp.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const Eigen::Vector4f &	centroid,
		Eigen::MatrixXf &	cloud_out
	)

Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.

Parameters:

cloud_in	the input point cloud
centroid	the centroid of the point cloud
cloud_out	the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns)

Definition at line 866 of file centroid.hpp.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const std::vector< int > &	indices,
		const Eigen::Vector4f &	centroid,
		Eigen::MatrixXf &	cloud_out
	)

Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.

Parameters:

cloud_in	the input point cloud
indices	the set of point indices to use from the input point cloud
centroid	the centroid of the point cloud
cloud_out	the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns)

Definition at line 884 of file centroid.hpp.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const pcl::PointIndices &	indices,
		const Eigen::Vector4f &	centroid,
		Eigen::MatrixXf &	cloud_out
	)

Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.

Parameters:

cloud_in	the input point cloud
indices	the set of point indices to use from the input point cloud
centroid	the centroid of the point cloud
cloud_out	the resultant output XYZ0 dimensions of cloud_in as an Eigen matrix (4 rows, N pts columns)

Definition at line 903 of file centroid.hpp.

template<typename Matrix >

Matrix::Scalar pcl::determinant3x3Matrix

(

const Matrix &

matrix

)

[inline]

Definition at line 700 of file eigen.h.

template<typename FloatVectorT >

float pcl::Div_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the div norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 175 of file norms.hpp.

template<typename Matrix , typename Vector >

void pcl::eigen22	(	const Matrix &	mat,
		typename Matrix::Scalar &	eigenvalue,
		Vector &	eigenvector
	)		[inline]

determine the smallest eigenvalue and its corresponding eigenvector

Parameters:

[in]	mat	input matrix that needs to be symmetric and positive semi definite
[out]	eigenvalue	the smallest eigenvalue of the input matrix
[out]	eigenvector	the corresponding eigenvector to the smallest eigenvalue of the input matrix

Definition at line 213 of file eigen.h.

template<typename Matrix , typename Vector >

void pcl::eigen22	(	const Matrix &	mat,
		Matrix &	eigenvectors,
		Vector &	eigenvalues
	)		[inline]

determine the smallest eigenvalue and its corresponding eigenvector

Parameters:

[in]	mat	input matrix that needs to be symmetric and positive semi definite
[out]	eigenvectors	the corresponding eigenvector to the smallest eigenvalue of the input matrix
[out]	eigenvalues	the smallest eigenvalue of the input matrix

Definition at line 257 of file eigen.h.

template<typename Matrix , typename Vector >

void pcl::eigen33	(	const Matrix &	mat,
		typename Matrix::Scalar &	eigenvalue,
		Vector &	eigenvector
	)		[inline]

determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi definite input matrix

Parameters:

[in]	mat	symmetric positive semi definite input matrix
[out]	eigenvalue	smallest eigenvalue of the input matrix
[out]	eigenvector	the corresponding eigenvector for the input eigenvalue

Note:

if the smallest eigenvalue is not unique, this function may return any eigenvector that is consistent to the eigenvalue.

Definition at line 354 of file eigen.h.

template<typename Matrix , typename Vector >

void pcl::eigen33	(	const Matrix &	mat,
		Vector &	evals
	)		[inline]

determines the eigenvalues of the symmetric positive semi definite input matrix

Parameters:

[in]	mat	symmetric positive semi definite input matrix
[out]	evals	resulting eigenvalues in ascending order

Definition at line 395 of file eigen.h.

template<typename Matrix , typename Vector >

void pcl::eigen33	(	const Matrix &	mat,
		Matrix &	evecs,
		Vector &	evals
	)		[inline]

determines the eigenvalues and corresponding eigenvectors of the symmetric positive semi definite input matrix

Parameters:

[in]	mat	symmetric positive semi definite input matrix
[out]	evecs	resulting eigenvalues in ascending order
[out]	evals	corresponding eigenvectors in correct order according to eigenvalues

Definition at line 414 of file eigen.h.

template<typename PointType1 , typename PointType2 >

float pcl::euclideanDistance	(	const PointType1 &	p1,
		const PointType2 &	p2
	)		[inline]

Calculate the euclidean distance between the two given points.

Parameters:

[in]	p1	the first point
[in]	p2	the second point

Definition at line 184 of file common/include/pcl/common/distances.h.

template<typename PointT >

void pcl::extractEuclideanClusters	(	const PointCloud< PointT > &	cloud,
		const boost::shared_ptr< search::Search< PointT > > &	tree,
		float	tolerance,
		std::vector< PointIndices > &	clusters,
		unsigned int	min_pts_per_cluster = 1,
		unsigned int	max_pts_per_cluster = (std::numeric_limits<int>::max) ()
	)

Decompose a region of space into clusters based on the Euclidean distance between points.

Parameters:

cloud	the point cloud message
tree	the spatial locator (e.g., kd-tree) used for nearest neighbors searching

Note:

the tree has to be created as a spatial locator on cloud

Parameters:

tolerance	the spatial cluster tolerance as a measure in L2 Euclidean space
clusters	the resultant clusters containing point indices (as a vector of PointIndices)
min_pts_per_cluster	minimum number of points that a cluster may contain (default: 1)
max_pts_per_cluster	maximum number of points that a cluster may contain (default: max int)

Definition at line 45 of file extract_clusters.hpp.

template<typename PointT >

void pcl::extractEuclideanClusters	(	const PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		const boost::shared_ptr< search::Search< PointT > > &	tree,
		float	tolerance,
		std::vector< PointIndices > &	clusters,
		unsigned int	min_pts_per_cluster = 1,
		unsigned int	max_pts_per_cluster = (std::numeric_limits<int>::max) ()
	)

Decompose a region of space into clusters based on the Euclidean distance between points.

Parameters:

cloud	the point cloud message
indices	a list of point indices to use from cloud
tree	the spatial locator (e.g., kd-tree) used for nearest neighbors searching

Note:

the tree has to be created as a spatial locator on cloud and indices

Parameters:

tolerance	the spatial cluster tolerance as a measure in L2 Euclidean space
clusters	the resultant clusters containing point indices (as a vector of PointIndices)
min_pts_per_cluster	minimum number of points that a cluster may contain (default: 1)
max_pts_per_cluster	maximum number of points that a cluster may contain (default: max int)

Todo:

: fix the return value, make sure the exit is not needed anymore

Definition at line 116 of file extract_clusters.hpp.

template<typename PointT , typename Normal >

void pcl::extractEuclideanClusters	(	const PointCloud< PointT > &	cloud,
		const PointCloud< Normal > &	normals,
		float	tolerance,
		const boost::shared_ptr< KdTree< PointT > > &	tree,
		std::vector< PointIndices > &	clusters,
		double	eps_angle,
		unsigned int	min_pts_per_cluster = 1,
		unsigned int	max_pts_per_cluster = (std::numeric_limits<int>::max) ()
	)

Decompose a region of space into clusters based on the euclidean distance between points, and the normal angular deviation.

Parameters:

cloud	the point cloud message
normals	the point cloud message containing normal information
tree	the spatial locator (e.g., kd-tree) used for nearest neighbors searching

Note:

the tree has to be created as a spatial locator on cloud

Parameters:

tolerance	the spatial cluster tolerance as a measure in the L2 Euclidean space
clusters	the resultant clusters containing point indices (as a vector of PointIndices)
eps_angle	the maximum allowed difference between normals in radians for cluster/region growing
min_pts_per_cluster	minimum number of points that a cluster may contain (default: 1)
max_pts_per_cluster	maximum number of points that a cluster may contain (default: max int)

Definition at line 99 of file extract_clusters.h.

template<typename PointT , typename Normal >

void pcl::extractEuclideanClusters	(	const PointCloud< PointT > &	cloud,
		const PointCloud< Normal > &	normals,
		const std::vector< int > &	indices,
		const boost::shared_ptr< KdTree< PointT > > &	tree,
		float	tolerance,
		std::vector< PointIndices > &	clusters,
		double	eps_angle,
		unsigned int	min_pts_per_cluster = 1,
		unsigned int	max_pts_per_cluster = (std::numeric_limits<int>::max) ()
	)

Decompose a region of space into clusters based on the euclidean distance between points, and the normal angular deviation.

Parameters:

cloud	the point cloud message
normals	the point cloud message containing normal information
indices	a list of point indices to use from cloud
tree	the spatial locator (e.g., kd-tree) used for nearest neighbors searching

Note:

the tree has to be created as a spatial locator on cloud

Parameters:

tolerance	the spatial cluster tolerance as a measure in the L2 Euclidean space
clusters	the resultant clusters containing point indices (as PointIndices)
eps_angle	the maximum allowed difference between normals in degrees for cluster/region growing
min_pts_per_cluster	minimum number of points that a cluster may contain (default: 1)
max_pts_per_cluster	maximum number of points that a cluster may contain (default: max int)

Definition at line 198 of file extract_clusters.h.

template<typename PointT >

void pcl::extractLabeledEuclideanClusters	(	const PointCloud< PointT > &	cloud,
		const boost::shared_ptr< search::Search< PointT > > &	tree,
		float	tolerance,
		std::vector< std::vector< PointIndices > > &	labeled_clusters,
		unsigned int	min_pts_per_cluster = 1,
		unsigned int	max_pts_per_cluster = (std::numeric_limits<int>::max) (),
		unsigned int	max_label = (std::numeric_limits<int>::max)
	)

Decompose a region of space into clusters based on the Euclidean distance between points.

Parameters:

[in]	cloud	the point cloud message
[in]	tree	the spatial locator (e.g., kd-tree) used for nearest neighbors searching

Note:

the tree has to be created as a spatial locator on cloud

Parameters:

[in]	tolerance	the spatial cluster tolerance as a measure in L2 Euclidean space
[out]	labeled_clusters	the resultant clusters containing point indices (as a vector of PointIndices)
[in]	min_pts_per_cluster	minimum number of points that a cluster may contain (default: 1)
[in]	max_pts_per_cluster	maximum number of points that a cluster may contain (default: max int)
[in]	max_label

Definition at line 44 of file extract_labeled_clusters.hpp.

template<typename PointT , typename Scalar >

void pcl::flipNormalTowardsViewpoint	(	const PointT &	point,
		float	vp_x,
		float	vp_y,
		float	vp_z,
		Eigen::Matrix< Scalar, 4, 1 > &	normal
	)		[inline]

Flip (in place) the estimated normal of a point towards a given viewpoint.

Parameters:

point	a given point
vp_x	the X coordinate of the viewpoint
vp_y	the X coordinate of the viewpoint
vp_z	the X coordinate of the viewpoint
normal	the plane normal to be flipped

Definition at line 115 of file normal_3d.h.

template<typename PointT , typename Scalar >

void pcl::flipNormalTowardsViewpoint	(	const PointT &	point,
		float	vp_x,
		float	vp_y,
		float	vp_z,
		Eigen::Matrix< Scalar, 3, 1 > &	normal
	)		[inline]

Flip (in place) the estimated normal of a point towards a given viewpoint.

Parameters:

point	a given point
vp_x	the X coordinate of the viewpoint
vp_y	the X coordinate of the viewpoint
vp_z	the X coordinate of the viewpoint
normal	the plane normal to be flipped

Definition at line 142 of file normal_3d.h.

template<typename PointT >

void pcl::flipNormalTowardsViewpoint	(	const PointT &	point,
		float	vp_x,
		float	vp_y,
		float	vp_z,
		float &	nx,
		float &	ny,
		float &	nz
	)		[inline]

Flip (in place) the estimated normal of a point towards a given viewpoint.

Parameters:

point	a given point
vp_x	the X coordinate of the viewpoint
vp_y	the X coordinate of the viewpoint
vp_z	the X coordinate of the viewpoint
nx	the resultant X component of the plane normal
ny	the resultant Y component of the plane normal
nz	the resultant Z component of the plane normal

Definition at line 163 of file normal_3d.h.

template<typename Sequence , typename F >

void pcl::for_each_type

(

F

f

)

[inline]

Definition at line 91 of file for_each_type.h.

template<typename PointT >

void pcl::fromROSMsg	(	const sensor_msgs::PointCloud2 &	msg,
		pcl::PointCloud< PointT > &	cloud,
		const MsgFieldMap &	field_map
	)

Convert a PointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.

Parameters:

[in]	msg	the PointCloud2 binary blob
[out]	cloud	the resultant pcl::PointCloud<T>
[in]	field_map	a MsgFieldMap object

Note:

Use fromROSMsg (PointCloud2, PointCloud<T>) directly or create you own MsgFieldMap using:

 MsgFieldMap field_map;
 createMapping<PointT> (msg.fields, field_map);

Definition at line 163 of file conversions.h.

template<typename PointT >

void pcl::fromROSMsg	(	const sensor_msgs::PointCloud2 &	msg,
		pcl::PointCloud< PointT > &	cloud
	)

Convert a PointCloud2 binary data blob into a pcl::PointCloud<T> object.

Parameters:

[in]	msg	the PointCloud2 binary blob
[out]	cloud	the resultant pcl::PointCloud<T>

Definition at line 221 of file conversions.h.

Eigen::MatrixXi pcl::getAllNeighborCellIndices

(

)

[inline]

Get the relative cell indices of all the 26 neighbors.

Note:

Useful in combination with getNeighborCentroidIndices() from VoxelGrid

Definition at line 123 of file voxel_grid.h.

void pcl::getAllPcdFilesInDirectory	(	const std::string &	directory,
		std::vector< std::string > &	file_names
	)		[inline]

Find all *.pcd files in the directory and return them sorted.

Parameters:

directory	the directory to be searched
file_names	the resulting (sorted) list of .pcd files

Definition at line 40 of file file_io.hpp.

double pcl::getAngle3D	(	const Eigen::Vector4f &	v1,
		const Eigen::Vector4f &	v2
	)		[inline]

Compute the smallest angle between two vectors in the [ 0, PI ) interval in 3D.

Parameters:

v1	the first 3D vector (represented as a Eigen::Vector4f)
v2	the second 3D vector (represented as a Eigen::Vector4f)

Returns:

the angle between v1 and v2

Definition at line 45 of file common.hpp.

template<typename PointT >

void pcl::getApproximateIndices	(	const typename pcl::PointCloud< PointT >::Ptr &	cloud_in,
		const typename pcl::PointCloud< PointT >::Ptr &	cloud_ref,
		std::vector< int > &	indices
	)

Get a set of approximate indices for a given point cloud into a reference point cloud. The coordinates of the two point clouds can differ. The method uses an internal KdTree for finding the closest neighbors from cloud_in in cloud_ref.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	cloud_ref	the reference point cloud dataset
[out]	indices	the resultant set of nearest neighbor indices of cloud_in in cloud_ref

Definition at line 68 of file kdtree/include/pcl/kdtree/impl/io.hpp.

template<typename Point1T , typename Point2T >

void pcl::getApproximateIndices	(	const typename pcl::PointCloud< Point1T >::Ptr &	cloud_in,
		const typename pcl::PointCloud< Point2T >::Ptr &	cloud_ref,
		std::vector< int > &	indices
	)

Get a set of approximate indices for a given point cloud into a reference point cloud. The coordinates of the two point clouds can differ. The method uses an internal KdTree for finding the closest neighbors from cloud_in in cloud_ref.

Parameters:

[in]	cloud_in	the input point cloud dataset
[in]	cloud_ref	the reference point cloud dataset
[out]	indices	the resultant set of nearest neighbor indices of cloud_in in cloud_ref

Definition at line 48 of file kdtree/include/pcl/kdtree/impl/io.hpp.

template<typename PointT >

double pcl::getCircumcircleRadius	(	const PointT &	pa,
		const PointT &	pb,
		const PointT &	pc
	)		[inline]

Compute the radius of a circumscribed circle for a triangle formed of three points pa, pb, and pc.

Parameters:

pa	the first point
pb	the second point
pc	the third point

Returns:

the radius of the circumscribed circle

Definition at line 353 of file common.hpp.

bool pcl::getEigenAsPointCloud	(	Eigen::MatrixXf &	in,
		sensor_msgs::PointCloud2 &	out
	)

Copy the XYZ dimensions from an Eigen MatrixXf into a sensor_msgs::PointCloud2 message.

Parameters:

[in]	in	the Eigen MatrixXf format containing XYZ0 / point
[out]	out	the resultant point cloud message

Note:

the method assumes that the PointCloud2 message already has the fields set up properly !

Definition at line 296 of file common/src/io.cpp.

void pcl::getEulerAngles	(	const Eigen::Affine3f &	t,
		float &	roll,
		float &	pitch,
		float &	yaw
	)		[inline]

Extract the Euler angles (XYZ-convention) from the given transformation.

Parameters:

[in]	t	the input transformation matrix
[in]	roll	the resulting roll angle
[in]	pitch	the resulting pitch angle
[in]	yaw	the resulting yaw angle

Definition at line 96 of file eigen.hpp.

template<int N>

void pcl::getFeaturePointCloud	(	const std::vector< Eigen::MatrixXf, Eigen::aligned_allocator< Eigen::MatrixXf > > &	histograms2D,
		PointCloud< Histogram< N > > &	histogramsPC
	)

Transform a list of 2D matrices into a point cloud containing the values in a vector (Histogram<N>). Can be used to transform the 2D histograms obtained in RSDEstimation into a point cloud.

Note:

The template paramter N should be (greater or) equal to the product of the number of rows and columns.

Parameters:

[in]	histograms2D	the list of neighborhood 2D histograms
[out]	histogramsPC	the dataset containing the linearized matrices

Definition at line 53 of file rsd.h.

int pcl::getFieldIndex	(	const sensor_msgs::PointCloud2 &	cloud,
		const std::string &	field_name
	)		[inline]

Get the index of a specified field (i.e., dimension/channel)

Parameters:

[in]	cloud	the the point cloud message
[in]	field_name	the string defining the field name

Definition at line 58 of file common/include/pcl/common/io.h.

template<typename PointT >

int pcl::getFieldIndex	(	const pcl::PointCloud< PointT > &	cloud,
		const std::string &	field_name,
		std::vector< sensor_msgs::PointField > &	fields
	)		[inline]

Get the index of a specified field (i.e., dimension/channel)

Parameters:

[in]	cloud	the the point cloud message
[in]	field_name	the string defining the field name
[out]	fields	a vector to the original PointField vector that the raw PointCloud message contains

Definition at line 47 of file common/include/pcl/common/impl/io.hpp.

template<typename PointT >

int pcl::getFieldIndex	(	const std::string &	field_name,
		std::vector< sensor_msgs::PointField > &	fields
	)		[inline]

Get the index of a specified field (i.e., dimension/channel)

Parameters:

[in]	field_name	the string defining the field name
[out]	fields	a vector to the original PointField vector that the raw PointCloud message contains

Definition at line 62 of file common/include/pcl/common/impl/io.hpp.

template<typename PointT >

void pcl::getFields	(	const pcl::PointCloud< PointT > &	cloud,
		std::vector< sensor_msgs::PointField > &	fields
	)		[inline]

Get the list of available fields (i.e., dimension/channel)

Parameters:

[in]	cloud	the point cloud message
[out]	fields	a vector to the original PointField vector that the raw PointCloud message contains

Definition at line 76 of file common/include/pcl/common/impl/io.hpp.

template<typename PointT >

void pcl::getFields

(

std::vector< sensor_msgs::PointField > &

fields

)

[inline]

Get the list of available fields (i.e., dimension/channel)

Parameters:

[out]

fields

a vector to the original PointField vector that the raw PointCloud message contains

Definition at line 85 of file common/include/pcl/common/impl/io.hpp.

int pcl::getFieldSize

(

const int

datatype

)

[inline]

Obtains the size of a specific field data type in bytes.

Parameters:

[in]

datatype

the field data type (see PointField.h)

Definition at line 127 of file common/include/pcl/common/io.h.

template<typename PointT >

std::string pcl::getFieldsList

(

const pcl::PointCloud< PointT > &

cloud

)

[inline]

Get the list of all fields available in a given cloud.

Parameters:

[in]

cloud

the the point cloud message

Definition at line 94 of file common/include/pcl/common/impl/io.hpp.

std::string pcl::getFieldsList

(

const sensor_msgs::PointCloud2 &

cloud

)

[inline]

Get the available point cloud fields as a space separated string.

Parameters:

[in]

cloud

a pointer to the PointCloud message

Definition at line 113 of file common/include/pcl/common/io.h.

PCL_EXPORTS void pcl::getFieldsSizes	(	const std::vector< sensor_msgs::PointField > &	fields,
		std::vector< int > &	field_sizes
	)

Obtain a vector with the sizes of all valid fields (e.g., not "_")

Parameters:

[in]	fields	the input vector containing the fields
[out]	field_sizes	the resultant field sizes in bytes

Definition at line 45 of file common/src/io.cpp.

int pcl::getFieldType	(	const int	size,
		char	type
	)		[inline]

Obtains the type of the PointField from a specific size and type.

Parameters:

[in]	size	the size in bytes of the data field
[in]	type	a char describing the type of the field ('F' = float, 'I' = signed, 'U' = unsigned)

Definition at line 166 of file common/include/pcl/common/io.h.

char pcl::getFieldType

(

const int

type

)

[inline]

Obtains the type of the PointField from a specific PointField as a char.

Parameters:

[in]

type

the PointField field type

Definition at line 204 of file common/include/pcl/common/io.h.

std::string pcl::getFileExtension

(

const std::string &

input

)

[inline]

Get the file extension from the given string (the remaining string after the last '.')

Parameters:

input

the input filename

Returns:

input 's file extension

Definition at line 75 of file file_io.hpp.

std::string pcl::getFilenameWithoutExtension

(

const std::string &

input

)

[inline]

Remove the extension from the given string and return only the filename (everything before the last '.')

Parameters:

input

the input filename (with the file extension)

Returns:

the resulting filename, stripped of its extension

Definition at line 69 of file file_io.hpp.

std::string pcl::getFilenameWithoutPath

(

const std::string &

input

)

[inline]

Remove the path from the given string and return only the filename (the remaining string after the last '/')

Parameters:

input

the input filename (with full path)

Returns:

the resulting filename, stripped of the path

Definition at line 63 of file file_io.hpp.

Eigen::MatrixXi pcl::getHalfNeighborCellIndices

(

)

[inline]

Get the relative cell indices of the "upper half" 13 neighbors.

Note:

Useful in combination with getNeighborCentroidIndices() from VoxelGrid

Definition at line 86 of file voxel_grid.h.

template<typename PointT >

void pcl::getMaxDistance	(	const pcl::PointCloud< PointT > &	cloud,
		const Eigen::Vector4f &	pivot_pt,
		Eigen::Vector4f &	max_pt
	)		[inline]

Get the point at maximum distance from a given point and a given pointcloud.

Parameters:

cloud	the point cloud data message
pivot_pt	the point from where to compute the distance
max_pt	the point in cloud that is the farthest point away from pivot_pt

Definition at line 115 of file common.hpp.

template<typename PointT >

void pcl::getMaxDistance	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		const Eigen::Vector4f &	pivot_pt,
		Eigen::Vector4f &	max_pt
	)		[inline]

Get the point at maximum distance from a given point and a given pointcloud.

Parameters:

cloud	the point cloud data message
pivot_pt	the point from where to compute the distance
indices	the vector of point indices to use from cloud
max_pt	the point in cloud that is the farthest point away from pivot_pt

Definition at line 158 of file common.hpp.

template<typename PointT >

double pcl::getMaxSegment	(	const pcl::PointCloud< PointT > &	cloud,
		PointT &	pmin,
		PointT &	pmax
	)		[inline]

Obtain the maximum segment in a given set of points, and return the minimum and maximum points.

Parameters:

[in]	cloud	the point cloud dataset
[out]	pmin	the coordinates of the "minimum" point in cloud (one end of the segment)
[out]	pmax	the coordinates of the "maximum" point in cloud (the other end of the segment)

Returns:

the length of segment length

Definition at line 100 of file common/include/pcl/common/distances.h.

template<typename PointT >

double pcl::getMaxSegment	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		PointT &	pmin,
		PointT &	pmax
	)		[inline]

Obtain the maximum segment in a given set of points, and return the minimum and maximum points.

Parameters:

[in]	cloud	the point cloud dataset
[in]	indices	a set of point indices to use from cloud
[out]	pmin	the coordinates of the "minimum" point in cloud (one end of the segment)
[out]	pmax	the coordinates of the "maximum" point in cloud (the other end of the segment)

Returns:

the length of segment length

Definition at line 139 of file common/include/pcl/common/distances.h.

void pcl::getMeanStd	(	const std::vector< float > &	values,
		double &	mean,
		double &	stddev
	)		[inline]

Compute both the mean and the standard deviation of an array of values.

Parameters:

values	the array of values
mean	the resultant mean of the distribution
stddev	the resultant standard deviation of the distribution

Definition at line 56 of file common.hpp.

void pcl::getMeanStdDev	(	const std::vector< float > &	values,
		double &	mean,
		double &	stddev
	)

Compute both the mean and the standard deviation of an array of values.

Parameters:

values	the array of values
mean	the resultant mean of the distribution
stddev	the resultant standard deviation of the distribution

Definition at line 72 of file common/src/common.cpp.

template<typename PointT >

void pcl::getMinMax	(	const PointT &	histogram,
		int	len,
		float &	min_p,
		float &	max_p
	)		[inline]

Get the minimum and maximum values on a point histogram.

Parameters:

histogram	the point representing a multi-dimensional histogram
len	the length of the histogram
min_p	the resultant minimum
max_p	the resultant maximum

Definition at line 370 of file common.hpp.

void pcl::getMinMax	(	const sensor_msgs::PointCloud2 &	cloud,
		int	idx,
		const std::string &	field_name,
		float &	min_p,
		float &	max_p
	)

Get the minimum and maximum values on a point histogram.

Parameters:

cloud	the cloud containing multi-dimensional histograms
idx	point index representing the histogram that we need to compute min/max for
field_name	the field name containing the multi-dimensional histogram
min_p	the resultant minimum
max_p	the resultant maximum

Definition at line 43 of file common/src/common.cpp.

void pcl::getMinMax3D	(	const sensor_msgs::PointCloud2ConstPtr &	cloud,
		int	x_idx,
		int	y_idx,
		int	z_idx,
		Eigen::Vector4f &	min_pt,
		Eigen::Vector4f &	max_pt
	)

Obtain the maximum and minimum points in 3D from a given point cloud.

Parameters:

[in]	cloud	the pointer to a sensor_msgs::PointCloud2 dataset
[in]	x_idx	the index of the X channel
[in]	y_idx	the index of the Y channel
[in]	z_idx	the index of the Z channel
[out]	min_pt	the minimum data point
[out]	max_pt	the maximum data point

Definition at line 47 of file filters/src/voxel_grid.cpp.

void pcl::getMinMax3D	(	const sensor_msgs::PointCloud2ConstPtr &	cloud,
		int	x_idx,
		int	y_idx,
		int	z_idx,
		const std::string &	distance_field_name,
		float	min_distance,
		float	max_distance,
		Eigen::Vector4f &	min_pt,
		Eigen::Vector4f &	max_pt,
		bool	limit_negative = false
	)

Obtain the maximum and minimum points in 3D from a given point cloud.

Note:

Performs internal data filtering as well.

Parameters:

[in]	cloud	the pointer to a sensor_msgs::PointCloud2 dataset
[in]	x_idx	the index of the X channel
[in]	y_idx	the index of the Y channel
[in]	z_idx	the index of the Z channel
[in]	distance_field_name	the name of the dimension to filter data along to
[in]	min_distance	the minimum acceptable value in distance_field_name data
[in]	max_distance	the maximum acceptable value in distance_field_name data
[out]	min_pt	the minimum data point
[out]	max_pt	the maximum data point
[in]	limit_negative	false if data inside of the [min_distance; max_distance] interval should be considered, true otherwise.

Definition at line 92 of file filters/src/voxel_grid.cpp.

template<typename PointT >

void pcl::getMinMax3D	(	const pcl::PointCloud< PointT > &	cloud,
		PointT &	min_pt,
		PointT &	max_pt
	)		[inline]

Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.

Parameters:

cloud	the point cloud data message
min_pt	the resultant minimum bounds
max_pt	the resultant maximum bounds

Definition at line 205 of file common.hpp.

template<typename PointT >

void pcl::getMinMax3D	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Vector4f &	min_pt,
		Eigen::Vector4f &	max_pt
	)		[inline]

Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.

Parameters:

cloud	the point cloud data message
min_pt	the resultant minimum bounds
max_pt	the resultant maximum bounds

Definition at line 242 of file common.hpp.

template<typename PointT >

void pcl::getMinMax3D	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		Eigen::Vector4f &	min_pt,
		Eigen::Vector4f &	max_pt
	)		[inline]

Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.

Parameters:

cloud	the point cloud data message
indices	the vector of point indices to use from cloud
min_pt	the resultant minimum bounds
max_pt	the resultant maximum bounds

Definition at line 318 of file common.hpp.

template<typename PointT >

void pcl::getMinMax3D	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		Eigen::Vector4f &	min_pt,
		Eigen::Vector4f &	max_pt
	)		[inline]

Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.

Parameters:

cloud	the point cloud data message
indices	the vector of point indices to use from cloud
min_pt	the resultant minimum bounds
max_pt	the resultant maximum bounds

Definition at line 280 of file common.hpp.

template<typename PointT >

void pcl::getMinMax3D	(	const typename pcl::PointCloud< PointT >::ConstPtr &	cloud,
		const std::string &	distance_field_name,
		float	min_distance,
		float	max_distance,
		Eigen::Vector4f &	min_pt,
		Eigen::Vector4f &	max_pt,
		bool	limit_negative = false
	)

Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud, without considering points outside of a distance threshold from the laser origin.

Parameters:

[in]	cloud	the point cloud data message
[in]	distance_field_name	the field name that contains the distance values
[in]	min_distance	the minimum distance a point will be considered from
[in]	max_distance	the maximum distance a point will be considered to
[out]	min_pt	the resultant minimum bounds
[out]	max_pt	the resultant maximum bounds
[in]	limit_negative	if set to true, then all points outside of the interval (min_distance;max_distace) are considered

Definition at line 46 of file voxel_grid.hpp.

bool pcl::getPointCloudAsEigen	(	const sensor_msgs::PointCloud2 &	in,
		Eigen::MatrixXf &	out
	)

Copy the XYZ dimensions of a sensor_msgs::PointCloud2 into Eigen format.

Parameters:

[in]	in	the point cloud message
[out]	out	the resultant Eigen MatrixXf format containing XYZ0 / point

Definition at line 254 of file common/src/io.cpp.

template<typename PointT >

void pcl::getPointCloudDifference	(	const pcl::PointCloud< PointT > &	src,
		const pcl::PointCloud< PointT > &	tgt,
		double	threshold,
		const boost::shared_ptr< pcl::search::Search< PointT > > &	tree,
		pcl::PointCloud< PointT > &	output
	)

Obtain the difference between two aligned point clouds as another point cloud, given a distance threshold.

Parameters:

src	the input point cloud source
tgt	the input point cloud target we need to obtain the difference against
threshold	the distance threshold (tolerance) for point correspondences. (e.g., check if f a point p1 from src has a correspondence > threshold than a point p2 from tgt)
tree	the spatial locator (e.g., kd-tree) used for nearest neighbors searching built over tgt
output	the resultant output point cloud difference

Definition at line 46 of file segment_differences.hpp.

template<typename PointT >

void pcl::getPointsInBox	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Vector4f &	min_pt,
		Eigen::Vector4f &	max_pt,
		std::vector< int > &	indices
	)		[inline]

Get a set of points residing in a box given its bounds.

Parameters:

cloud	the point cloud data message
min_pt	the minimum bounds
max_pt	the maximum bounds
indices	the resultant set of point indices residing in the box

Definition at line 72 of file common.hpp.

void pcl::getRejectedQueryIndices	(	const pcl::Correspondences &	correspondences_before,
		const pcl::Correspondences &	correspondences_after,
		std::vector< int > &	indices,
		bool	presorting_required = true
	)

Get the query points of correspondences that are present in one correspondence vector but not in the other, e.g., to compare correspondences before and after rejection.

Parameters:

[in]	correspondences_before	Vector of correspondences before rejection
[in]	correspondences_after	Vector of correspondences after rejection
[out]	indices	Query point indices of correspondences that have been rejected
[in]	presorting_required	Enable/disable internal sorting of vectors. By default (true), vectors are internally sorted before determining their difference. If the order of correspondences in correspondences_after is not different (has not been changed) from the order in correspondences_before this pre-processing step can be disabled in order to gain efficiency. In order to disable pre-sorting set presorting_requered to false.

Definition at line 43 of file correspondence.cpp.

double pcl::getTime

(

)

[inline]

Definition at line 142 of file common/time.h.

void pcl::getTransformation	(	float	x,
		float	y,
		float	z,
		float	roll,
		float	pitch,
		float	yaw,
		Eigen::Affine3f &	t
	)		[inline]

Create a transformation from the given translation and Euler angles (XYZ-convention)

Parameters:

[in]	x	the input x translation
[in]	y	the input y translation
[in]	z	the input z translation
[in]	roll	the input roll angle
[in]	pitch	the input pitch angle
[in]	yaw	the input yaw angle
[out]	t	the resulting transformation matrix

Definition at line 113 of file eigen.hpp.

Eigen::Affine3f pcl::getTransformation	(	float	x,
		float	y,
		float	z,
		float	roll,
		float	pitch,
		float	yaw
	)		[inline]

Create a transformation from the given translation and Euler angles (XYZ-convention)

Parameters:

[in]	x	the input x translation
[in]	y	the input y translation
[in]	z	the input z translation
[in]	roll	the input roll angle
[in]	pitch	the input pitch angle
[in]	yaw	the input yaw angle

Returns:

the resulting transformation matrix

Definition at line 123 of file eigen.hpp.

void pcl::getTransformationFromTwoUnitVectors	(	const Eigen::Vector3f &	y_direction,
		const Eigen::Vector3f &	z_axis,
		Eigen::Affine3f &	transformation
	)		[inline]

Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)

Parameters:

[in]	y_direction	the y direction
[in]	z_axis	the z-axis
[out]	transformation	the resultant 3D rotation

Definition at line 76 of file eigen.hpp.

Eigen::Affine3f pcl::getTransformationFromTwoUnitVectors	(	const Eigen::Vector3f &	y_direction,
		const Eigen::Vector3f &	z_axis
	)		[inline]

Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)

Parameters:

[in]	y_direction	the y direction
[in]	z_axis	the z-axis

Returns:

transformation the resultant 3D rotation

Definition at line 81 of file eigen.hpp.

void pcl::getTransformationFromTwoUnitVectorsAndOrigin	(	const Eigen::Vector3f &	y_direction,
		const Eigen::Vector3f &	z_axis,
		const Eigen::Vector3f &	origin,
		Eigen::Affine3f &	transformation
	)		[inline]

Get the transformation that will translate orign to (0,0,0) and rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)

Parameters:

[in]	y_direction	the y direction
[in]	z_axis	the z-axis
[in]	origin	the origin
[in]	transformation	the resultant transformation matrix

Definition at line 88 of file eigen.hpp.

void pcl::getTransFromUnitVectorsXY	(	const Eigen::Vector3f &	x_axis,
		const Eigen::Vector3f &	y_direction,
		Eigen::Affine3f &	transformation
	)		[inline]

Get the unique 3D rotation that will rotate x_axis into (1,0,0) and y_direction into a vector with z=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)

Parameters:

[in]	x_axis	the x-axis
[in]	y_direction	the y direction
[out]	transformation	the resultant 3D rotation

Definition at line 57 of file eigen.hpp.

Eigen::Affine3f pcl::getTransFromUnitVectorsXY	(	const Eigen::Vector3f &	x_axis,
		const Eigen::Vector3f &	y_direction
	)		[inline]

Get the unique 3D rotation that will rotate x_axis into (1,0,0) and y_direction into a vector with z=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)

Parameters:

[in]	x_axis	the x-axis
[in]	y_direction	the y direction

Returns:

the resulting 3D rotation

Definition at line 69 of file eigen.hpp.

void pcl::getTransFromUnitVectorsZY	(	const Eigen::Vector3f &	z_axis,
		const Eigen::Vector3f &	y_direction,
		Eigen::Affine3f &	transformation
	)		[inline]

Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)

Parameters:

[in]	z_axis	the z-axis
[in]	y_direction	the y direction
[out]	transformation	the resultant 3D rotation

Definition at line 38 of file eigen.hpp.

Eigen::Affine3f pcl::getTransFromUnitVectorsZY	(	const Eigen::Vector3f &	z_axis,
		const Eigen::Vector3f &	y_direction
	)		[inline]

Get the unique 3D rotation that will rotate z_axis into (0,0,1) and y_direction into a vector with x=0 (or into (0,1,0) should y_direction be orthogonal to z_axis)

Parameters:

[in]	z_axis	the z-axis
[in]	y_direction	the y direction

Returns:

the resultant 3D rotation

Definition at line 50 of file eigen.hpp.

void pcl::getTranslationAndEulerAngles	(	const Eigen::Affine3f &	t,
		float &	x,
		float &	y,
		float &	z,
		float &	roll,
		float &	pitch,
		float &	yaw
	)		[inline]

Extract x,y,z and the Euler angles (XYZ-convention) from the given transformation

Parameters:

[in]	t	the input transformation matrix
[out]	x	the resulting x translation
[out]	y	the resulting y translation
[out]	z	the resulting z translation
[out]	roll	the resulting roll angle
[out]	pitch	the resulting pitch angle
[out]	yaw	the resulting yaw angle

Definition at line 103 of file eigen.hpp.

template<typename FloatVectorT >

float pcl::HIK_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the HIK norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 225 of file norms.hpp.

template<typename Matrix >

Matrix::Scalar pcl::invert2x2	(	const Matrix &	matrix,
		Matrix &	inverse
	)		[inline]

Calculate the inverse of a 2x2 matrix.

Parameters:

[in]	matrix	matrix to be inverted
[out]	inverse	the resultant inverted matrix

Note:

only the upper triangular part is taken into account => non symmetric matrices will give wrong results

Returns:

determinant of the original matrix => if 0 no inverse exists => result is invalid

Definition at line 603 of file eigen.h.

template<typename Matrix >

Matrix::Scalar pcl::invert3x3Matrix	(	const Matrix &	matrix,
		Matrix &	inverse
	)		[inline]

Calculate the inverse of a general 3x3 matrix.

Parameters:

[in]	matrix	matrix to be inverted
[out]	inverse	the resultant inverted matrix

Returns:

determinant of the original matrix => if 0 no inverse exists => result is invalid

Definition at line 668 of file eigen.h.

template<typename Matrix >

Matrix::Scalar pcl::invert3x3SymMatrix	(	const Matrix &	matrix,
		Matrix &	inverse
	)		[inline]

Calculate the inverse of a 3x3 symmetric matrix.

Parameters:

[in]	matrix	matrix to be inverted
[out]	inverse	the resultant inverted matrix

Note:

only the upper triangular part is taken into account => non symmetric matrices will give wrong results

Returns:

determinant of the original matrix => if 0 no inverse exists => result is invalid

Definition at line 628 of file eigen.h.

bool pcl::isBetterCorrespondence	(	const Correspondence &	pc1,
		const Correspondence &	pc2
	)		[inline]

Comparator to enable us to sort a vector of PointCorrespondences according to their scores using std::sort (begin(), end(), isBetterCorrespondence);.

Definition at line 157 of file correspondence.h.

template<typename PointT >

bool pcl::isFinite

(

const PointT &

pt

)

[inline]

Tests if the 3D components of a point are all finite param[in] pt point to be tested

Definition at line 1309 of file point_types.hpp.

template<>

bool pcl::isFinite< pcl::Normal >

(

const pcl::Normal &

n

)

[inline]

Definition at line 1316 of file point_types.hpp.

template<typename PointT >

bool pcl::isPointIn2DPolygon	(	const PointT &	point,
		const pcl::PointCloud< PointT > &	polygon
	)

General purpose method for checking if a 3D point is inside or outside a given 2D polygon.

Note:

this method accepts any general 3D point that is projected onto the 2D polygon, but performs an internal XY projection of both the polygon and the point.

Parameters:

point	a 3D point projected onto the same plane as the polygon
polygon	a polygon

Definition at line 47 of file extract_polygonal_prism_data.hpp.

template<typename Type >

bool pcl::isValueFinite	(	const sensor_msgs::PointCloud2 &	cloud,
		const unsigned int	point_index,
		const int	point_size,
		const unsigned int	field_idx,
		const unsigned int	fields_count
	)		[inline]

Check whether a given value of type Type (uchar, char, uint, int, float, double, ...) is finite or not.

Parameters:

[in]	cloud	the cloud that contains the data
[in]	point_index	the index of the point
[in]	point_size	the size of the point in the cloud
[in]	field_idx	the index of the dimension/field
[in]	fields_count	the current fields count

Returns:

true if the value is finite, false otherwise

Definition at line 292 of file io/include/pcl/io/file_io.h.

bool pcl::isVisible	(	const Eigen::Vector2f &	X,
		const Eigen::Vector2f &	S1,
		const Eigen::Vector2f &	S2,
		const Eigen::Vector2f &	R = Eigen::Vector2f::Zero ()
	)		[inline]

Returns if a point X is visible from point R (or the origin) when taking into account the segment between the points S1 and S2.

Parameters:

X	2D coordinate of the point
S1	2D coordinate of the segment's first point
S2	2D coordinate of the segment's secont point
R	2D coorddinate of the reference point (defaults to 0,0)

Definition at line 72 of file gp3.h.

template<typename PointT >

bool pcl::isXYPointIn2DXYPolygon	(	const PointT &	point,
		const pcl::PointCloud< PointT > &	polygon
	)

Check if a 2d point (X and Y coordinates considered only!) is inside or outside a given polygon. This method assumes that both the point and the polygon are projected onto the XY plane.

Note:

(This is highly optimized code taken from http://www.visibone.com/inpoly/) Copyright (c) 1995-1996 Galacticomm, Inc. Freeware source code.

Parameters:

point	a 3D point projected onto the same plane as the polygon
polygon	a polygon

Definition at line 107 of file extract_polygonal_prism_data.hpp.

template<typename FloatVectorT >

float pcl::JM_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the JM norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 120 of file norms.hpp.

template<typename FloatVectorT >

float pcl::K_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim,
		float	P1,
		float	P2
	)		[inline]

Compute the K norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)
P1	the first parameter
P2	the second parameter

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 200 of file norms.hpp.

template<typename FloatVectorT >

float pcl::KL_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the KL between two discrete probability density functions.

Parameters:

A	the first discrete PDF
B	the second discrete PDF
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 211 of file norms.hpp.

template<typename FloatVectorT >

float pcl::L1_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the L1 norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 80 of file norms.hpp.

template<typename FloatVectorT >

float pcl::L2_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the L2 norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 103 of file norms.hpp.

template<typename FloatVectorT >

float pcl::L2_Norm_SQR	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the squared L2 norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 90 of file norms.hpp.

void pcl::lineToLineSegment	(	const Eigen::VectorXf &	line_a,
		const Eigen::VectorXf &	line_b,
		Eigen::Vector4f &	pt1_seg,
		Eigen::Vector4f &	pt2_seg
	)

Get the shortest 3D segment between two 3D lines.

Parameters:

line_a	the coefficients of the first line (point, direction)
line_b	the coefficients of the second line (point, direction)
pt1_seg	the first point on the line segment
pt2_seg	the second point on the line segment

Definition at line 40 of file distances.cpp.

bool pcl::lineWithLineIntersection	(	const Eigen::VectorXf &	line_a,
		const Eigen::VectorXf &	line_b,
		Eigen::Vector4f &	point,
		double	sqr_eps = 1e-4
	)

Get the intersection of a two 3D lines in space as a 3D point.

Parameters:

line_a	the coefficients of the first line (point, direction)
line_b	the coefficients of the second line (point, direction)
point	holder for the computed 3D point
sqr_eps	maximum allowable squared distance to the true solution

Definition at line 41 of file intersections.cpp.

bool pcl::lineWithLineIntersection	(	const pcl::ModelCoefficients &	line_a,
		const pcl::ModelCoefficients &	line_b,
		Eigen::Vector4f &	point,
		double	sqr_eps = 1e-4
	)

Get the intersection of a two 3D lines in space as a 3D point.

Parameters:

line_a	the coefficients of the first line (point, direction)
line_b	the coefficients of the second line (point, direction)
point	holder for the computed 3D point
sqr_eps	maximum allowable squared distance to the true solution

Definition at line 60 of file intersections.cpp.

template<typename FloatVectorT >

float pcl::Linf_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the L-infinity norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 110 of file norms.hpp.

template<typename Derived >

void pcl::loadBinary	(	Eigen::MatrixBase< Derived > const &	matrix,
		std::istream &	file
	)

Read a matrix from an input stream.

Parameters:

[out]	matrix	the resulting matrix, read from the input stream
[in,out]	file	the input stream

Definition at line 145 of file eigen.hpp.

unsigned int pcl::lzfCompress	(	const void *const	in_data,
		unsigned int	in_len,
		void *	out_data,
		unsigned int	out_len
	)

Compress in_len bytes stored at the memory block starting at in_data and write the result to out_data, up to a maximum length of out_len bytes using Marc Lehmann's LZF algorithm.

If the output buffer is not large enough or any error occurs return 0, otherwise return the number of bytes used, which might be considerably more than in_len (but less than 104% of the original size), so it makes sense to always use out_len == in_len - 1), to ensure _some_ compression, and store the data uncompressed otherwise (with a flag, of course.

Note:

The buffers must not be overlapping.

Parameters:

[in]	in_data	the input uncompressed buffer
[in]	in_len	the length of the input buffer
[out]	out_data	the output buffer where the compressed result will be stored
[out]	out_len	the length of the output buffer

Definition at line 84 of file lzf.cpp.

unsigned int pcl::lzfDecompress	(	const void *const	in_data,
		unsigned int	in_len,
		void *	out_data,
		unsigned int	out_len
	)

Decompress data compressed with the lzfCompress function and stored at location in_data and length in_len. The result will be stored at out_data up to a maximum of out_len characters.

If the output buffer is not large enough to hold the decompressed data, a 0 is returned and errno is set to E2BIG. Otherwise the number of decompressed bytes (i.e. the original length of the data) is returned.

If an error in the compressed data is detected, a zero is returned and errno is set to EINVAL.

This function is very fast, about as fast as a copying loop.

Parameters:

[in]	in_data	the input compressed buffer
[in]	in_len	the length of the input buffer
[out]	out_data	the output buffer (must be resized to out_len)
[out]	out_len	the length of the output buffer

Definition at line 276 of file lzf.cpp.

float pcl::normAngle

(

float

alpha

)

[inline]

Normalize an angle to (-PI, PI].

Parameters:

alpha

the input angle (in radians)

Definition at line 42 of file angles.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const RangeImageBorderExtractor::Parameters &	p
	)		[inline]

Definition at line 57 of file range_image_border_extractor.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const Correspondence &	c
	)		[inline]

overloaded << operator

Definition at line 86 of file correspondence.h.

template<typename real >

std::ostream & pcl::operator<<	(	std::ostream &	os,
		const BivariatePolynomialT< real > &	p
	)

Definition at line 230 of file bivariate_polynomial.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const NarfKeypoint::Parameters &	p
	)		[inline]

Definition at line 191 of file narf_keypoint.h.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXYZ &	p
	)		[inline]

Definition at line 212 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXYZI &	p
	)		[inline]

Definition at line 277 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXYZL &	p
	)		[inline]

Definition at line 301 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const Label &	p
	)		[inline]

Definition at line 312 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXYZRGBA &	p
	)		[inline]

Definition at line 361 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXYZRGB &	p
	)		[inline]

Definition at line 444 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXYZRGBL &	p
	)		[inline]

Definition at line 473 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXYZHSV &	p
	)		[inline]

Definition at line 512 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXY &	p
	)		[inline]

Definition at line 527 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const InterestPoint &	p
	)		[inline]

Definition at line 549 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const Normal &	p
	)		[inline]

Definition at line 588 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const Axis &	p
	)		[inline]

Definition at line 619 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const _Axis &	p
	)		[inline]

Definition at line 625 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointNormal &	p
	)		[inline]

Definition at line 659 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXYZRGBNormal &	p
	)		[inline]

Definition at line 736 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointXYZINormal &	p
	)		[inline]

Definition at line 772 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointWithRange &	p
	)		[inline]

Definition at line 805 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const RangeImage &	r
	)		[inline]

/ingroup range_image

Definition at line 813 of file range_image.h.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointWithViewpoint &	p
	)		[inline]

Definition at line 839 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const MomentInvariants &	p
	)		[inline]

Definition at line 852 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PrincipalRadiiRSD &	p
	)		[inline]

Definition at line 865 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const Boundary &	p
	)		[inline]

Definition at line 878 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PrincipalCurvatures &	p
	)		[inline]

Definition at line 902 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PFHSignature125 &	p
	)		[inline]

Definition at line 915 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PFHRGBSignature250 &	p
	)		[inline]

Definition at line 929 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PPFSignature &	p
	)		[inline]

Definition at line 944 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PPFRGBSignature &	p
	)		[inline]

Definition at line 959 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const NormalBasedSignature12 &	p
	)		[inline]

Definition at line 974 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const ShapeContext &	p
	)		[inline]

Definition at line 990 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const SHOT &	p
	)		[inline]

Definition at line 1009 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const SHOT352 &	p
	)		[inline]

Definition at line 1027 of file point_types.hpp.

template<typename PointT >

std::ostream& pcl::operator<<	(	std::ostream &	s,
		const pcl::PointCloud< PointT > &	p
	)

Definition at line 1034 of file point_cloud.h.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const SHOT1344 &	p
	)		[inline]

Definition at line 1045 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const ReferenceFrame &	p
	)		[inline]

Definition at line 1100 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const FPFHSignature33 &	p
	)		[inline]

Definition at line 1113 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const VFHSignature308 &	p
	)		[inline]

Definition at line 1127 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const ESFSignature640 &	p
	)		[inline]

Definition at line 1141 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const GFPFHSignature16 &	p
	)		[inline]

Definition at line 1156 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const Narf36 &	p
	)		[inline]

Definition at line 1171 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const BorderDescription &	p
	)		[inline]

Definition at line 1189 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const IntensityGradient &	p
	)		[inline]

Definition at line 1211 of file point_types.hpp.

template<int N>

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const Histogram< N > &	p
	)		[inline]

Definition at line 1226 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointWithScale &	p
	)		[inline]

Definition at line 1252 of file point_types.hpp.

std::ostream& pcl::operator<<	(	std::ostream &	os,
		const PointSurfel &	p
	)		[inline]

Definition at line 1291 of file point_types.hpp.

pcl::PCL_DEPRECATED	(	inline std::ostream &operator<<	std::ostream &os, const SHOT &p,
		"SHOT POINT IS	DEPRECATED,
		USE SHOT352 FOR SHAPE AND SHOT1344 FOR SHAPE+COLOR INSTEAD"
	)

template<typename PointInT , typename PointNT , typename PointRFT >

class pcl::PCL_DEPRECATED_CLASS	(	SHOTEstimationOMP	,
		"SHOTEstimationOMP<..., pcl::SHOT, ...> IS	DEPRECATED,
		USE SHOTEstimationOMP<..., pcl::SHOT352,...> INSTEAD"
	)

template<typename PointNT , typename PointRFT >

class pcl::PCL_DEPRECATED_CLASS	(	SHOTEstimationOMP	,
		"SHOTEstimationOMP<pcl::PointXYZRGBA,...,pcl::SHOT,...> IS	DEPRECATED,
		USE SHOTEstimationOMP< pcl::PointXYZRGBA,..., pcl::SHOT352,...> FOR SHAPE AND SHOTColorEstimationOMP< pcl::PointXYZRGBA,..., pcl::SHOT1344,...> FOR SHAPE+COLOR INSTEAD"
	)

Empty constructor.

Initialize the scheduler and set the number of threads to use.

Parameters:

nr_threads

the number of hardware threads to use (-1 sets the value back to automatic)

Estimate the Signatures of Histograms of OrienTations (SHOT) descriptors at a set of points given by <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in setSearchMethod ()

Parameters:

output

the resultant point cloud model dataset that contains the SHOT feature estimates

The number of threads the scheduler should use.

Definition at line 257 of file shot_omp.h.

template<typename PointInT , typename PointNT , typename PointRFT >

class pcl::PCL_DEPRECATED_CLASS	(	SHOTEstimation	,
		"SHOTEstimation<..., pcl::SHOT, ...> IS	DEPRECATED,
		USE SHOTEstimation<..., pcl::SHOT352,...> INSTEAD"
	)

SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals.

Note:

If you use this code in any academic work, please cite:

• F. Tombari, S. Salti, L. Di Stefano Unique Signatures of Histograms for Local Surface Description. In Proceedings of the 11th European Conference on Computer Vision (ECCV), Heraklion, Greece, September 5-11 2010.
• F. Tombari, S. Salti, L. Di Stefano A Combined Texture-Shape Descriptor For Enhanced 3D Feature Matching. In Proceedings of the 18th International Conference on Image Processing (ICIP), Brussels, Belgium, September 11-14 2011.

Author:

Samuele Salti, Federico Tombari

template<typename PointNT , typename PointRFT >

class pcl::PCL_DEPRECATED_CLASS	(	SHOTEstimation	,
		"SHOTEstimation<pcl::PointXYZRGBA,...,pcl::SHOT,...> IS	DEPRECATED,
		USE SHOTEstimation< pcl::PointXYZRGBA,..., pcl::SHOT352,...> FOR SHAPE AND SHOTColorEstimation< pcl::PointXYZRGBA,..., pcl::SHOT1344,...> FOR SHAPE+COLOR INSTEAD"
	)

SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals.

Note:

If you use this code in any academic work, please cite:

• F. Tombari, S. Salti, L. Di Stefano Unique Signatures of Histograms for Local Surface Description. In Proceedings of the 11th European Conference on Computer Vision (ECCV), Heraklion, Greece, September 5-11 2010.
• F. Tombari, S. Salti, L. Di Stefano A Combined Texture-Shape Descriptor For Enhanced 3D Feature Matching. In Proceedings of the 18th International Conference on Image Processing (ICIP), Brussels, Belgium, September 11-14 2011.

Author:

Samuele Salti, Federico Tombari

Empty constructor.

Parameters:

[in]	describe_shape
[in]	describe_color
[in]	nr_shape_bins
[in]	nr_color_bins

Estimate the SHOT descriptor for a given point based on its spatial neighborhood of 3D points with normals

Parameters:

[in]	index	the index of the point in indices_
[in]	indices	the k-neighborhood point indices in surface_
[in]	sqr_dists	the k-neighborhood point distances in surface_
[out]	shot	the resultant SHOT descriptor representing the feature at the query point

Estimate the Signatures of Histograms of OrienTations (SHOT) descriptors at a set of points given by <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in setSearchMethod ()

Parameters:

[out]

output

the resultant point cloud model dataset that contains the SHOT feature estimates

Quadrilinear interpolation; used when color and shape descriptions are both activated

Parameters:

[in]	indices	the neighborhood point indices
[in]	sqr_dists	the neighborhood point distances
[in]	index	the index of the point in indices_
[out]	binDistanceShape	the resultant distance shape histogram
[out]	binDistanceColor	the resultant color shape histogram
[in]	nr_bins_shape	the number of bins in the shape histogram
[in]	nr_bins_color	the number of bins in the color histogram
[out]	shot	the resultant SHOT histogram

Converts RGB triplets to CIELab space.

Parameters:

[in]	R	the red channel
[in]	G	the green channel
[in]	B	the blue channel
[out]	L	the lightness
[out]	A	the first color-opponent dimension
[out]	B2	the second color-opponent dimension

Compute shape descriptor.

Compute color descriptor.

The number of bins in each color histogram.

Definition at line 706 of file shot.h.

template<typename PointNT , typename PointRFT >

class pcl::PCL_DEPRECATED_CLASS	(	SHOTEstimation	,
		"SHOTEstimation<pcl::PointXYZRGBA,...,Eigen::MatrixXf,...> IS	DEPRECATED,
		USE SHOTColorEstimation< pcl::PointXYZRGBA,..., Eigen::MatrixXf,...> FOR SHAPE AND SHAPE+COLOR INSTEAD"
	)

SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals.

Note:

If you use this code in any academic work, please cite:

• F. Tombari, S. Salti, L. Di Stefano Unique Signatures of Histograms for Local Surface Description. In Proceedings of the 11th European Conference on Computer Vision (ECCV), Heraklion, Greece, September 5-11 2010.
• F. Tombari, S. Salti, L. Di Stefano A Combined Texture-Shape Descriptor For Enhanced 3D Feature Matching. In Proceedings of the 18th International Conference on Image Processing (ICIP), Brussels, Belgium, September 11-14 2011.

Author:

Samuele Salti, Federico Tombari

Empty constructor.

Parameters:

[in]	describe_shape
[in]	describe_color
[in]	nr_shape_bins
[in]	nr_color_bins

Estimate the Signatures of Histograms of OrienTations (SHOT) descriptors at a set of points given by <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in setSearchMethod ()

Parameters:

[out]

output

the resultant point cloud model dataset that contains the SHOT feature estimates

Make the compute (&PointCloudOut); inaccessible from outside the class

Parameters:

[out]

output

the output point cloud

Definition at line 838 of file shot.h.

template<typename FloatVectorT >

float pcl::PF_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim,
		float	P1,
		float	P2
	)		[inline]

Compute the PF norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)
P1	the first parameter
P2	the second parameter

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 189 of file norms.hpp.

bool pcl::planeWithPlaneIntersection	(	const Eigen::Vector4f &	plane_a,
		const Eigen::Vector4f &	fplane_b,
		Eigen::VectorXf &	line,
		double	angular_tolerance = 0.1
	)

Determine the line of intersection of two non-parallel planes using lagrange multipliers in: "Intersection of Two Planes, John Krumm, Microsoft Research, Redmond, WA, USA".

Parameters:

[in]	coefficients	of plane A and plane B in the form ax + by + cz + d = 0
[out]	coefficients	of line where line.tail<3>() = direction vector and line.head<3>() the point on the line clossest to (0, 0, 0)

Returns:

true if succeeded/planes aren't parallel

Definition at line 70 of file intersections.cpp.

void pcl::PointCloudXYZRGBtoXYZHSV	(	PointCloud< PointXYZRGB > &	in,
		PointCloud< PointXYZHSV > &	out
	)		[inline]

Convert a XYZRGB point cloud to a XYZHSV.

Parameters:

[in]	in	the input XYZRGB point cloud
[out]	out	the output XYZHSV point cloud

Definition at line 171 of file point_types_conversion.h.

void pcl::PointCloudXYZRGBtoXYZI	(	PointCloud< PointXYZRGB > &	in,
		PointCloud< PointXYZI > &	out
	)		[inline]

Convert a XYZRGB point cloud to a XYZI.

Parameters:

[in]	in	the input XYZRGB point cloud
[out]	out	the output XYZI point cloud

Definition at line 188 of file point_types_conversion.h.

template<typename Point >

double pcl::pointToPlaneDistance	(	const Point &	p,
		double	a,
		double	b,
		double	c,
		double	d
	)		[inline]

Get the distance from a point to a plane (unsigned) defined by ax+by+cz+d=0.

Parameters:

p	a point
a	the normalized a coefficient of a plane
b	the normalized b coefficient of a plane
c	the normalized c coefficient of a plane
d	the normalized d coefficient of a plane

Definition at line 107 of file sac_model_plane.h.

template<typename Point >

double pcl::pointToPlaneDistance	(	const Point &	p,
		const Eigen::Vector4f &	plane_coefficients
	)		[inline]

Get the distance from a point to a plane (unsigned) defined by ax+by+cz+d=0.

Parameters:

p	a point
plane_coefficients	the normalized coefficients (a, b, c, d) of a plane

Definition at line 118 of file sac_model_plane.h.

template<typename Point >

double pcl::pointToPlaneDistanceSigned	(	const Point &	p,
		double	a,
		double	b,
		double	c,
		double	d
	)		[inline]

Get the distance from a point to a plane (signed) defined by ax+by+cz+d=0.

Parameters:

p	a point
a	the normalized a coefficient of a plane
b	the normalized b coefficient of a plane
c	the normalized c coefficient of a plane
d	the normalized d coefficient of a plane

Definition at line 82 of file sac_model_plane.h.

template<typename Point >

double pcl::pointToPlaneDistanceSigned	(	const Point &	p,
		const Eigen::Vector4f &	plane_coefficients
	)		[inline]

Get the distance from a point to a plane (signed) defined by ax+by+cz+d=0.

Parameters:

p	a point
plane_coefficients	the normalized coefficients (a, b, c, d) of a plane

Definition at line 93 of file sac_model_plane.h.

void pcl::PointXYZHSVtoXYZRGB	(	PointXYZHSV &	in,
		PointXYZRGB &	out
	)		[inline]

Convert a XYZHSV point type to a XYZRGB.

Parameters:

[in]	in	the input XYZHSV point
[out]	out	the output XYZRGB point

Definition at line 103 of file point_types_conversion.h.

void pcl::PointXYZRGBtoXYZHSV	(	PointXYZRGB &	in,
		PointXYZHSV &	out
	)		[inline]

Convert a XYZRGB point type to a XYZHSV.

Parameters:

[in]	in	the input XYZRGB point
[out]	out	the output XYZHSV point

Definition at line 67 of file point_types_conversion.h.

void pcl::PointXYZRGBtoXYZI	(	PointXYZRGB &	in,
		PointXYZI &	out
	)		[inline]

Convert a XYZRGB point type to a XYZI.

Parameters:

[in]	in	the input XYZRGB point
[out]	out	the output XYZI point

Definition at line 54 of file point_types_conversion.h.

template<typename Point >

void pcl::projectPoint	(	const Point &	p,
		const Eigen::Vector4f &	model_coefficients,
		Point &	q
	)		[inline]

Project a point on a planar model given by a set of normalized coefficients.

Parameters:

[in]	p	the input point to project
[in]	model_coefficients	the coefficients of the plane (a, b, c, d) that satisfy ax+by+cz+d=0
[out]	q	the resultant projected point

Definition at line 55 of file sac_model_plane.h.

float pcl::rad2deg

(

float

alpha

)

[inline]

Convert an angle from radians to degrees.

Parameters:

alpha

the input angle (in radians)

Definition at line 55 of file angles.hpp.

double pcl::rad2deg

(

double

alpha

)

[inline]

Convert an angle from radians to degrees.

Parameters:

alpha

the input angle (in radians)

Definition at line 67 of file angles.hpp.

template<typename PointT >

void pcl::removeNaNFromPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		std::vector< int > &	index
	)

Removes points with x, y, or z equal to NaN.

Parameters:

cloud_in	the input point cloud
index	the mapping (ordered): cloud_out.points[i] = cloud_in.points[index[i]]

Note:

The density of the point cloud is lost.

Can be called with cloud_in == cloud_out

Definition at line 45 of file filter_indices.hpp.

template<typename PointT >

void pcl::removeNaNFromPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		std::vector< int > &	index
	)

Removes points with x, y, or z equal to NaN.

Parameters:

[in]	cloud_in	the input point cloud
[out]	cloud_out	the input point cloud
[out]	index	the mapping (ordered): cloud_out.points[i] = cloud_in.points[index[i]]

Note:

The density of the point cloud is lost.

Can be called with cloud_in == cloud_out

Definition at line 45 of file filter.hpp.

static const std::map<pcl::SacModel, unsigned int> pcl::SAC_SAMPLE_SIZE	(	sample_size_pairs	,
		sample_size_pairs+	sizeofsample_size_pairs)/sizeof(SampleSizeModel
	)		[static]

template<typename Derived >

void pcl::saveBinary	(	const Eigen::MatrixBase< Derived > &	matrix,
		std::ostream &	file
	)

Write a matrix to an output stream.

Parameters:

[in]	matrix	the matrix to output
[out]	file	the output stream

Definition at line 131 of file eigen.hpp.

template<typename FloatVectorT >

float pcl::selectNorm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim,
		NormType	norm_type
	)		[inline]

Method that calculates any norm type available, based on the norm_type variable.

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 42 of file norms.hpp.

void pcl::solvePlaneParameters	(	const Eigen::Matrix3f &	covariance_matrix,
		const Eigen::Vector4f &	point,
		Eigen::Vector4f &	plane_parameters,
		float &	curvature
	)		[inline]

Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.

Parameters:

covariance_matrix	the 3x3 covariance matrix
point	a point lying on the least-squares plane (SSE aligned)
plane_parameters	the resultant plane parameters as: a, b, c, d (ax + by + cz + d = 0)
curvature	the estimated surface curvature as a measure of

Definition at line 47 of file feature.hpp.

void pcl::solvePlaneParameters	(	const Eigen::Matrix3f &	covariance_matrix,
		float &	nx,
		float &	ny,
		float &	nz,
		float &	curvature
	)		[inline]

Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.

Parameters:

covariance_matrix	the 3x3 covariance matrix
nx	the resultant X component of the plane normal
ny	the resultant Y component of the plane normal
nz	the resultant Z component of the plane normal
curvature	the estimated surface curvature as a measure of

Definition at line 60 of file feature.hpp.

double pcl::sqrPointToLineDistance	(	const Eigen::Vector4f &	pt,
		const Eigen::Vector4f &	line_pt,
		const Eigen::Vector4f &	line_dir
	)		[inline]

Get the square distance from a point to a line (represented by a point and a direction)

Parameters:

pt	a point
line_pt	a point on the line (make sure that line_pt[3] = 0 as there are no internal checks!)
line_dir	the line direction

Definition at line 69 of file common/include/pcl/common/distances.h.

double pcl::sqrPointToLineDistance	(	const Eigen::Vector4f &	pt,
		const Eigen::Vector4f &	line_pt,
		const Eigen::Vector4f &	line_dir,
		const double	sqr_length
	)		[inline]

Get the square distance from a point to a line (represented by a point and a direction)

Note:

This one is useful if one has to compute many distances to a fixed line, so the vector length can be pre-computed

Parameters:

pt	a point
line_pt	a point on the line (make sure that line_pt[3] = 0 as there are no internal checks!)
line_dir	the line direction
sqr_length	the squared norm of the line direction

Definition at line 85 of file common/include/pcl/common/distances.h.

template<typename Scalar >

Scalar pcl::sqrt

(

const Scalar &

val

)

[inline]

Definition at line 96 of file eigen.h.

template<>

double pcl::sqrt< double >

(

const double &

val

)

[inline]

Definition at line 101 of file eigen.h.

template<>

float pcl::sqrt< float >

(

const float &

val

)

[inline]

Definition at line 107 of file eigen.h.

template<>

long double pcl::sqrt< long double >

(

const long double &

val

)

[inline]

Definition at line 113 of file eigen.h.

template<typename PointType1 , typename PointType2 >

float pcl::squaredEuclideanDistance	(	const PointType1 &	p1,
		const PointType2 &	p2
	)		[inline]

Calculate the squared euclidean distance between the two given points.

Parameters:

[in]	p1	the first point
[in]	p2	the second point

Definition at line 174 of file common/include/pcl/common/distances.h.

template<typename FloatVectorT >

float pcl::Sublinear_Norm	(	FloatVectorT	A,
		FloatVectorT	B,
		int	dim
	)		[inline]

Compute the sublinear norm of the vector between two points.

Parameters:

A	the first point
B	the second point
dim	the number of dimensions in A and B (dimensions must match)

Note:

FloatVectorT is any type of vector with its values accessible via [ ]

Definition at line 149 of file norms.hpp.

void pcl::throwPCLIOException	(	const char *	function_name,
		const char *	file_name,
		unsigned	line_number,
		const char *	format,
			...
	)		[inline]

Parameters:

[in]	function_name	the name of the method where the exception was caused
[in]	file_name	the name of the file where the exception was caused
[in]	line_number	the number of the line where the exception was caused
[in]	format	printf format

Definition at line 81 of file pcl_io_exception.h.

template<typename PointT >

void pcl::toROSMsg	(	const pcl::PointCloud< PointT > &	cloud,
		sensor_msgs::PointCloud2 &	msg
	)

Convert a pcl::PointCloud<T> object to a PointCloud2 binary data blob.

Parameters:

[in]	cloud	the input pcl::PointCloud<T>
[out]	msg	the resultant PointCloud2 binary blob

Todo:

msg.is_bigendian = ?;

Definition at line 233 of file conversions.h.

template<typename CloudT >

void pcl::toROSMsg	(	const CloudT &	cloud,
		sensor_msgs::Image &	msg
	)

Copy the RGB fields of a PointCloud into sensor_msgs::Image format.

Parameters:

[in]	cloud	the point cloud message
[out]	msg	the resultant sensor_msgs::Image CloudT cloud type, CloudT should be akin to pcl::PointCloud<pcl::PointXYZRGBA>

Note:

will throw std::runtime_error if there is a problem

Definition at line 271 of file conversions.h.

void pcl::toROSMsg	(	const sensor_msgs::PointCloud2 &	cloud,
		sensor_msgs::Image &	msg
	)		[inline]

Copy the RGB fields of a PointCloud2 msg into sensor_msgs::Image format.

Parameters:

cloud	the point cloud message
msg	the resultant sensor_msgs::Image will throw std::runtime_error if there is a problem

Definition at line 304 of file conversions.h.

template<typename PointT >

PointT pcl::transformPoint	(	const PointT &	point,
		const Eigen::Affine3f &	transform
	)		[inline]

Transform a point with members x,y,z.

Parameters:

[in]	point	the point to transform
[out]	transform	the transformation to apply

Returns:

the transformed point

Definition at line 290 of file transforms.hpp.

template<typename PointT >

void pcl::transformPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Affine3f &	transform
	)

Apply an affine transform defined by an Eigen Transform.

Parameters:

cloud_in	the input point cloud
cloud_out	the resultant output point cloud
transform	an affine transformation (typically a rigid transformation)

Note:

Can be used with cloud_in equal to cloud_out

Definition at line 39 of file transforms.hpp.

template<typename PointT >

void pcl::transformPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const std::vector< int > &	indices,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Affine3f &	transform
	)

Apply an affine transform defined by an Eigen Transform.

Parameters:

cloud_in	the input point cloud
indices	the set of point indices to use from the input point cloud
cloud_out	the resultant output point cloud
transform	an affine transformation (typically a rigid transformation)

Note:

Can be used with cloud_in equal to cloud_out

Definition at line 79 of file transforms.hpp.

template<typename PointT >

void pcl::transformPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Matrix4f &	transform
	)

Apply an affine transform defined by an Eigen Transform.

Parameters:

cloud_in	the input point cloud
cloud_out	the resultant output point cloud
transform	an affine transformation (typically a rigid transformation)

Note:

Can be used with cloud_in equal to cloud_out

Definition at line 169 of file transforms.hpp.

template<typename PointT >

void pcl::transformPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Vector3f &	offset,
		const Eigen::Quaternionf &	rotation
	)		[inline]

Apply a rigid transform defined by a 3D offset and a quaternion.

Parameters:

cloud_in	the input point cloud
cloud_out	the resultant output point cloud
offset	the translation component of the rigid transformation
rotation	the rotation component of the rigid transformation

Definition at line 262 of file transforms.hpp.

template<typename PointT >

void pcl::transformPointCloudWithNormals	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Affine3f &	transform
	)

Transform a point cloud and rotate its normals using an Eigen transform.

Parameters:

cloud_in	the input point cloud
cloud_out	the resultant output point cloud
transform	an affine transformation (typically a rigid transformation)

Note:

Can be used with cloud_in equal to cloud_out

Definition at line 120 of file transforms.hpp.

template<typename PointT >

void pcl::transformPointCloudWithNormals	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Matrix4f &	transform
	)

Transform a point cloud and rotate its normals using an Eigen transform.

Parameters:

cloud_in	the input point cloud
cloud_out	the resultant output point cloud
transform	an affine transformation (typically a rigid transformation)

Note:

Can be used with cloud_in equal to cloud_out

Definition at line 210 of file transforms.hpp.

template<typename PointT >

void pcl::transformPointCloudWithNormals	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Vector3f &	offset,
		const Eigen::Quaternionf &	rotation
	)		[inline]

Transform a point cloud and rotate its normals using an Eigen transform.

Parameters:

cloud_in	the input point cloud
cloud_out	the resultant output point cloud
offset	the translation component of the rigid transformation
rotation	the rotation component of the rigid transformation

Definition at line 276 of file transforms.hpp.

---

Variable Documentation

class<SHOT> pcl::__pad0__

Definition at line 451 of file point_representation.h.

const unsigned int pcl::edgeTable[256]

Definition at line 59 of file marching_cubes.h.

struct pcl::_PointXYZHSV pcl::EIGEN_ALIGN16

const int pcl::I_SHIFT_EDGE[3][2]

Initial value:

 {
    {0,1}, {1,3}, {1,2}
  }

Definition at line 58 of file grid_projection.h.

const int pcl::I_SHIFT_EP[12][2]

Initial value:

 {
    {0, 4}, {1, 5}, {2, 6}, {3, 7}, 
    {0, 1}, {1, 2}, {2, 3}, {3, 0},
    {4, 5}, {5, 6}, {6, 7}, {7, 4}
  }

The 12 edges of a cell.

Definition at line 48 of file grid_projection.h.

const int pcl::I_SHIFT_PT[4]

Initial value:

 {
    0, 4, 5, 7
  }

Definition at line 54 of file grid_projection.h.

const int pcl::SAC_LMEDS = 1 [static]

Definition at line 44 of file method_types.h.

const int pcl::SAC_MLESAC = 5 [static]

Definition at line 48 of file method_types.h.

const int pcl::SAC_MSAC = 2 [static]

Definition at line 45 of file method_types.h.

const int pcl::SAC_PROSAC = 6 [static]

Definition at line 49 of file method_types.h.

const int pcl::SAC_RANSAC = 0 [static]

Definition at line 43 of file method_types.h.

const int pcl::SAC_RMSAC = 4 [static]

Definition at line 47 of file method_types.h.

const int pcl::SAC_RRANSAC = 3 [static]

Definition at line 46 of file method_types.h.

const int pcl::triTable[256][16]

Definition at line 93 of file marching_cubes.h.