pcl Namespace Reference

Namespaces
namespace	apps
namespace	cloud_composer
namespace	common
namespace	ComparisonOps
namespace	console
namespace	detail
namespace	distances
namespace	features
namespace	fields
namespace	filters
namespace	geometry
namespace	ihs
namespace	io
namespace	ism
namespace	keypoints
namespace	modeler
namespace	ndt2d
namespace	occlusion_reasoning
namespace	octree
namespace	on_nurbs
namespace	outofcore
namespace	people
namespace	poisson
namespace	recognition
namespace	registration
namespace	search
namespace	segmentation
namespace	surface
namespace	test
namespace	texture_mapping
namespace	tracking
namespace	traits
namespace	utils
namespace	visualization
Classes
struct	_Axis
struct	_Intensity
struct	_Intensity32u
struct	_Intensity8u
struct	_Normal
struct	_PointNormal
struct	_PointSurfel
struct	_PointWithRange
struct	_PointWithScale
struct	_PointWithViewpoint
struct	_PointXYZ
struct	_PointXYZHSV
struct	_PointXYZI
	A point structure representing Euclidean xyz coordinates, and the intensity value. More...
struct	_PointXYZINormal
struct	_PointXYZL
struct	_PointXYZRGB
struct	_PointXYZRGBA
struct	_PointXYZRGBL
struct	_PointXYZRGBNormal
struct	_ReferenceFrame
	A structure representing the Local Reference Frame of a point. More...
struct	_RGB
class	AdaptiveRangeCoder
	AdaptiveRangeCoder compression class More...
class	AgastKeypoint2D
	Detects 2D AGAST corner points. Based on the original work and paper reference by. More...
class	AgastKeypoint2D< pcl::PointXYZ, pcl::PointUV >
	Detects 2D AGAST corner points. Based on the original work and paper reference by. More...
class	AgastKeypoint2DBase
	Detects 2D AGAST corner points. Based on the original work and paper reference by. More...
class	ApproximateVoxelGrid
	ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...
class	ASCIIReader
	Ascii Point Cloud Reader. Read any ASCII file by setting the separating characters and input point fields. More...
struct	Axis
	A point structure representing an Axis using its normal coordinates. (SSE friendly) More...
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...
class	BOARDLocalReferenceFrameEstimation
	BOARDLocalReferenceFrameEstimation implements the BOrder Aware Repeatable Directions algorithm for local reference frame estimation as described here: 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...
struct	BoundingBoxXYZ
class	BoxClipper3D
	Implementation of a box clipper in 3D. Actually it allows affine transformations, thus any parallelepiped in general pose. The affine transformation is used to transform the point before clipping it using the unit cube centered at origin and with an extend of -1 to +1 in each dimension. More...
class	Clipper3D
	Base class for 3D clipper objects. More...
struct	cloud_show
struct	cloud_show_base
class	CloudIterator
	Iterator class for point clouds with or without given indices. More...
class	CloudSurfaceProcessing
	CloudSurfaceProcessing represents the base class for algorithms that takes a point cloud as input and produces 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	ColorGradientDOTModality
class	ColorGradientModality
	Modality based on max-RGB gradients. More...
class	ColorModality
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	ConditionalEuclideanClustering
	ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined clustering condition. More...
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...
class	ConstCloudIterator
	Iterator class for point clouds with or without given indices. 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	CorrespondenceGrouping
	Abstract base class for Correspondence Grouping algorithms. More...
class	CovarianceSampling
	Point Cloud sampling based on the 6D covariances. It selects the points such that the resulting cloud is as stable as possible for being registered (against a copy of itself) with ICP. The algorithm adds points to the resulting cloud incrementally, while trying to keep all the 6 eigenvalues of the covariance matrix as close to each other as possible. This class also comes with the computeConditionNumber method that returns a number which shows how stable a point cloud will be when used as input for ICP (the closer the value it is to 1.0, the better). More...
class	CrfNormalSegmentation
class	CRHAlignment
	CRHAlignment uses two Camera Roll Histograms (CRH) to find the roll rotation that aligns both views. See: More...
class	CRHEstimation
	CRHEstimation estimates the Camera Roll Histogram (CRH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in: More...
class	CropBox
	CropBox is a filter that allows the user to filter all the data inside of a given box. More...
class	CropBox< pcl::PCLPointCloud2 >
	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	DefaultIterator
class	DefaultPointRepresentation
	DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types. More...
class	DefaultPointRepresentation< FPFHSignature33 >
class	DefaultPointRepresentation< Narf36 >
class	DefaultPointRepresentation< NormalBasedSignature12 >
class	DefaultPointRepresentation< PFHRGBSignature250 >
class	DefaultPointRepresentation< PFHSignature125 >
class	DefaultPointRepresentation< PointNormal >
class	DefaultPointRepresentation< PointXYZ >
class	DefaultPointRepresentation< PointXYZI >
class	DefaultPointRepresentation< PPFSignature >
class	DefaultPointRepresentation< ShapeContext1980 >
class	DefaultPointRepresentation< SHOT1344 >
class	DefaultPointRepresentation< SHOT352 >
class	DefaultPointRepresentation< VFHSignature308 >
struct	DenseQuantizedMultiModTemplate
struct	DenseQuantizedSingleModTemplate
class	DifferenceOfNormalsEstimation
	A Difference of Normals (DoN) scale filter implementation for point cloud data. More...
class	DinastGrabber
	Grabber for DINAST devices (i.e., IPA-1002, IPA-1110, IPA-2001) More...
class	DistanceMap
	Represents a distance map obtained from a distance transformation. More...
class	DOTMOD
	Template matching using the DOTMOD approach. More...
class	DOTModality
struct	DOTMODDetection
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	EnergyMaps
	Stores a set of energy maps. 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< pcl::PCLPointCloud2 >
	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	FastBilateralFilter
	Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper: * Sylvain Paris and FrŽdo Durand "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach" European Conference on Computer Vision (ECCV'06) More...
class	FastBilateralFilterOMP
	Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper: * Sylvain Paris and FrÂŽdo Durand "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach" European Conference on Computer Vision (ECCV'06) 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	FileGrabber
	FileGrabber provides a container-style interface for grabbers which operate on fixed-size input. More...
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< pcl::PCLPointCloud2 >
	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< pcl::PCLPointCloud2 >
	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	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...
class	FrustumCulling
	FrustumCulling filters points inside a frustum given by pose and field of view of the camera. 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...
class	GeometricConsistencyGrouping
	Class implementing a 3D correspondence grouping enforcing geometric consistency among feature correspondences. More...
class	GFPFHEstimation
	GFPFHEstimation estimates the Global Fast Point Feature Histogram (GFPFH) descriptor for a given point cloud dataset containing points and labels. More...
struct	GFPFHSignature16
	A point structure representing the GFPFH descriptor with 16 bins. More...
class	GlobalHypothesesVerification
	A hypothesis verification method proposed in "A Global Hypotheses Verification Method for 3D Object Recognition", A. Aldoma and F. Tombari and L. Di Stefano and Markus Vincze, ECCV 2012. More...
class	Grabber
	Grabber interface for PCL 1.x device drivers. More...
class	GrabCut
	Implementation of the GrabCut segmentation in "GrabCut — Interactive Foreground Extraction using Iterated Graph Cuts" by Carsten Rother, Vladimir Kolmogorov and Andrew Blake. More...
struct	GradientXY
	A point structure representing Euclidean xyz coordinates, and the intensity value. More...
class	GraphRegistration
	GraphRegistration class is the base class for graph-based registration methods 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	GreedyVerification
	A greedy hypothesis verification method. More...
class	GridProjection
	Grid projection surface reconstruction method. More...
class	GroundPlaneComparator
	GroundPlaneComparator is a Comparator for detecting smooth surfaces suitable for driving. In conjunction with OrganizedConnectedComponentSegmentation, this allows smooth groundplanes / road surfaces to be segmented from point clouds. More...
class	HarrisKeypoint2D
	HarrisKeypoint2D detects Harris corners family points. More...
class	HarrisKeypoint3D
	HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals. More...
class	HarrisKeypoint6D
	Keypoint detector for detecting corners in 3D (XYZ), 2D (intensity) AND mixed versions of these. More...
class	HDLGrabber
	Grabber for the Velodyne High-Definition-Laser (HDL) More...
struct	Histogram
	A point structure representing an N-D histogram. More...
class	Hough3DGrouping
	Class implementing a 3D correspondence grouping algorithm that can deal with multiple instances of a model template found into a given scene. Each correspondence casts a vote for a reference point in a 3D Hough Space. The remaining 3 DOF are taken into account by associating each correspondence with a local Reference Frame. The suggested PointModelRfT is pcl::ReferenceFrame. More...
class	HypothesisVerification
	Abstract class for hypotheses verification methods. More...
class	ImageGrabber
class	ImageGrabberBase
	Base class for Image file grabber. 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	Intensity
	A point structure representing the grayscale intensity in single-channel images. Intensity is represented as a float value. More...
struct	Intensity32u
	A point structure representing the grayscale intensity in single-channel images. Intensity is represented as a uint8_t value. More...
struct	Intensity8u
	A point structure representing the grayscale intensity in single-channel images. Intensity is represented as a uint8_t value. More...
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	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...
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 PCLPointCloud2 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...
struct	ISMPeak
	This struct is used for storing peak. More...
class	IsNotDenseException
	An exception that is thrown when a PointCloud is not dense but is attemped to be used as dense. More...
class	ISSKeypoint3D
	ISSKeypoint3D detects the Intrinsic Shape Signatures keypoints for a given point cloud. This class is based on a particular implementation made by Federico Tombari and Samuele Salti and it has been explicitly adapted to PCL. 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	IterativeClosestPointWithNormals
	IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformation estimated based on Point to Plane distances by default. More...
class	IteratorIdx
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	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	LinearizedMaps
	Stores a set of linearized maps. More...
class	LinearLeastSquaresNormalEstimation
	Surface normal estimation on dense data using a least-squares estimation based on a first-order Taylor approximation. 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	LINEMOD
	Template matching using the LINEMOD approach. More...
struct	LINEMOD_OrientationMap
	Map that stores orientations. More...
struct	LINEMODDetection
	Represents a detection of a template using the LINEMOD approach. More...
class	LineRGBD
	High-level class for template matching using the LINEMOD approach based on RGB and Depth data. 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	MaskMap
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	MedianFilter
	Implementation of the median filter. The median filter is one of the simplest and wide-spread image processing filters. It is known to perform well with "shot"/impulse noise (some individual pixels having extreme values), it does not reduce contrast across steps in the function (as compared to filters based on averaging), and it is robust to outliers. Furthermore, it is simple to implement and efficient, as it requires a single pass over the image. It consists of a moving window of fixed size that replaces the pixel in the center with the median inside the window. 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	MeshQuadricDecimationVTK
	PCL mesh decimation based on vtkQuadricDecimation 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	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	ModelCoefficients
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	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	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. See B. Steder, R. B. Rusu, K. Konolige, and W. Burgard Point Feature Extraction on 3D Range Scans Taking into Account Object Boundaries In Proc. of the IEEE Int. Conf. on Robotics &Automation (ICRA). 2011. 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 See B. Steder, R. B. Rusu, K. Konolige, and W. Burgard Point Feature Extraction on 3D Range Scans Taking into Account Object Boundaries In Proc. of the IEEE Int. Conf. on Robotics &Automation (ICRA). 2011. 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
	A point structure representing normal coordinates and the surface curvature estimate. (SSE friendly) More...
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	NormalDistributionsTransform
	A 3D Normal Distribution Transform registration implementation for point cloud data. More...
class	NormalDistributionsTransform2D
	NormalDistributionsTransform2D provides an implementation of the Normal Distributions Transform algorithm for scan matching. 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	NormalEstimationOMP
	NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard. More...
class	NormalRefinement
	Normal vector refinement class 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	OURCVFHEstimation
	OURCVFHEstimation estimates the Oriented, Unique and Repetable Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset given XYZ data and normals, as presented in: More...
class	PackedHSIComparison
	A packed HSI specialization of the comparison object. More...
class	PackedRGBComparison
	A packed rgb specialization of the comparison object. More...
class	PairwiseGraphRegistration
	PairwiseGraphRegistration class aligns the clouds two by two More...
class	PapazovHV
	A hypothesis verification method proposed in "An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes", C. Papazov and D. Burschka, ACCV 2010. More...
class	PassThrough
	PassThrough passes points in a cloud based on constraints for one particular field of the point type. More...
class	PassThrough< pcl::PCLPointCloud2 >
	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 most PCL algorithms. More...
class	PCLBase< pcl::PCLPointCloud2 >
class	PCLException
	A base class for all pcl exceptions which inherits from std::runtime_error. More...
struct	PCLHeader
struct	PCLImage
struct	PCLPointCloud2
struct	PCLPointField
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	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...
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	PointIndices
struct	PointNormal
	A point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate. (SSE friendly) More...
class	PointRepresentation
	PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector. More...
struct	PointRGB
	A point structure for representing RGB color. 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	PointUV
	A 2D point structure representing pixel image coordinates. 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
	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
	A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate. More...
struct	PointXYZL
struct	PointXYZRGB
	A point structure representing Euclidean xyz coordinates, and the RGB color. More...
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...
class	Poisson
	The Poisson surface reconstruction algorithm. More...
struct	PolygonMesh
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	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...
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< pcl::PCLPointCloud2 >
	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	QuantizableModality
	Interface for a quantizable modality. More...
class	QuantizedMap
struct	QuantizedMultiModFeature
	Feature that defines a position and quantized value in a specific modality. More...
struct	QuantizedNormalLookUpTable
	Look-up-table for fast surface normal quantization. More...
class	RadiusOutlierRemoval
	RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have. More...
class	RadiusOutlierRemoval< pcl::PCLPointCloud2 >
	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< pcl::PCLPointCloud2 >
	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...
class	RangeImageSpherical
	RangeImageSpherical is derived from the original range image and uses a slightly different spherical projection. In the original range image, the image will appear more and more "scaled down" along the y axis, the further away from the mean line of the image a point is. This class removes this scaling, which makes it especially suitable for spinning LIDAR sensors that capure a 360° view, since a rotation of the sensor will now simply correspond to a shift of the range image. (This class is similar to RangeImagePlanar, but changes less of the behaviour of the base class.) More...
struct	ReferenceFrame
class	Region3D
	Region3D represents summary statistics of a 3D collection of points. More...
class	RegionGrowing
	Implements the well known Region Growing algorithm used for segmentation. Description can be found in the article "Segmentation of point clouds using smoothness constraint" by T. Rabbania, F. A. van den Heuvelb, G. Vosselmanc. In addition to residual test, the possibility to test curvature is added. More...
class	RegionGrowingRGB
	Implements the well known Region Growing algorithm used for segmentation based on color of points. Description can be found in the article "Color-based segmentation of point clouds" by Qingming Zhan, Yubin Liang, Yinghui Xiao. More...
struct	RegionXY
	Defines a region in XY-space. More...
class	Registration
	Registration represents the base registration class for general purpose, ICP-like methods. 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	RobotEyeGrabber
	Grabber for the Ocular Robotics RobotEye sensor. 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	SampleConsensusModelCircle3D
	SampleConsensusModelCircle3D defines a model for 3D circle segmentation. 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	SampleConsensusModelRegistration2D
	SampleConsensusModelRegistration2D defines a model for Point-To-Point registration outlier rejection using distances between 2D pixels. 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	SampleConsensusPrerejective
	Pose estimation and alignment class using a prerejective RANSAC routine. More...
class	SamplingSurfaceNormal
	SamplingSurfaceNormal divides the input space into grids until each grid contains a maximum of N points, and samples points randomly within each grid. Normal is computed using the N points of each grid. All points sampled within a grid are assigned the same normal. More...
class	ScopeTime
	Class to measure the time spent in a scope. More...
class	SeededHueSegmentation
	SeededHueSegmentation. 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...
class	ShadowPoints
	ShadowPoints removes the ghost points appearing on edge discontinuties More...
struct	ShapeContext1980
	A point structure representing a Shape Context. More...
class	ShapeContext3DEstimation
	ShapeContext3DEstimation implements the 3D shape context descriptor as described in: 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	SHOTColorEstimationOMP
	SHOTColorEstimationOMP estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors, in parallel, using the OpenMP standard. More...
class	SHOTEstimation
	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
	SHOTEstimationOMP 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...
struct	SparseQuantizedMultiModTemplate
	A multi-modality template constructed from a set of quantized multi-modality features. More...
class	SpinImageEstimation
	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< pcl::PCLPointCloud2 >
	StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check: More...
class	StopWatch
	Simple stopwatch. More...
class	Supervoxel
	Supervoxel container class - stores a cluster extracted using supervoxel clustering. More...
class	SupervoxelClustering
	Implements a supervoxel algorithm based on voxel structure, normals, and rgb values. More...
class	SurfaceNormalModality
	Modality based on surface normals. 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	SUSANKeypoint
	SUSANKeypoint implements a RGB-D extension of the SUSAN detector inluding normal directions variation in top of intensity variation. It is different from Harris in that it exploits normals directly so it is faster. Original paper "SUSAN — A New Approach to Low Level Image Processing", Smith, Stephen M. and Brady, J. Michael. More...
class	SynchronizedQueue
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 Context 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...
struct	Vertices
	Describes a set of vertices in a polygon mesh, by basically storing an array of indices. 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< pcl::PCLPointCloud2 >
	VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...
class	VoxelGridCovariance
	A searchable voxel strucure containing the mean and covariance of the data. More...
class	VoxelGridLabel
class	VoxelGridOcclusionEstimation
	VoxelGrid to estimate occluded space in the scene. The ray traversal algorithm is implemented by the work of 'John Amanatides and Andrew Woo, A Fast Voxel Traversal Algorithm for Ray Tracing'. More...
class	VTKUtils
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 < PCLHeader const >	HeaderConstPtr
typedef boost::shared_ptr < PCLHeader >	HeaderPtr
typedef std::vector < pcl::PointIndices >	IndicesClusters
typedef boost::shared_ptr < std::vector < pcl::PointIndices > >	IndicesClustersPtr
typedef boost::shared_ptr < const std::vector< int > >	IndicesConstPtr
typedef boost::shared_ptr < std::vector< int > >	IndicesPtr
typedef pcl::PointCloud < pcl::PointXYZ >	mc
typedef boost::shared_ptr < ::pcl::ModelCoefficients const >	ModelCoefficientsConstPtr
typedef boost::shared_ptr < ::pcl::ModelCoefficients >	ModelCoefficientsPtr
typedef std::vector < detail::FieldMapping >	MsgFieldMap
typedef boost::shared_ptr < ::pcl::PCLImage const >	PCLImageConstPtr
typedef boost::shared_ptr < ::pcl::PCLImage >	PCLImagePtr
typedef boost::shared_ptr < ::pcl::PCLPointCloud2 const >	PCLPointCloud2ConstPtr
typedef boost::shared_ptr < ::pcl::PCLPointCloud2 >	PCLPointCloud2Ptr
typedef boost::shared_ptr < ::pcl::PCLPointField const >	PCLPointFieldConstPtr
typedef boost::shared_ptr < ::pcl::PCLPointField >	PCLPointFieldPtr
typedef std::vector < PointCorrespondence3D, Eigen::aligned_allocator < PointCorrespondence3D > >	PointCorrespondences3DVector
typedef std::vector < PointCorrespondence6D, Eigen::aligned_allocator < PointCorrespondence6D > >	PointCorrespondences6DVector
typedef boost::shared_ptr < ::pcl::PointIndices const >	PointIndicesConstPtr
typedef boost::shared_ptr < ::pcl::PointIndices >	PointIndicesPtr
typedef boost::shared_ptr < ::pcl::PolygonMesh const >	PolygonMeshConstPtr
typedef boost::shared_ptr < ::pcl::PolygonMesh >	PolygonMeshPtr
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
typedef boost::shared_ptr < Vertices const >	VerticesConstPtr
typedef boost::shared_ptr < Vertices >	VerticesPtr
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_REGISTRATION_2D, 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 NormalT >
std::vector< float >	assignNormalWeights (const PointCloud< NormalT > &, int, const std::vector< int > &k_indices, const std::vector< float > &k_sqr_distances)
	Assign weights of nearby normals used for refinement.
template<typename FloatVectorT >
float	B_Norm (FloatVectorT A, FloatVectorT B, int dim)
	Compute the B norm of the vector between two points.
template<typename PointT >
float	calculatePolygonArea (const pcl::PointCloud< PointT > &polygon)
	Calculate the area of a polygon given a point cloud that defines the polygon.
bool	compareLabeledPointClusters (const pcl::PointIndices &a, const pcl::PointIndices &b)
	Sort clusters method (for std::sort).
bool	comparePair (std::pair< float, int > i, std::pair< float, int > j)
	This function is used as a comparator for sorting.
bool	comparePointClusters (const pcl::PointIndices &a, const pcl::PointIndices &b)
	Sort clusters method (for std::sort).
template<typename PointT , typename Scalar >
unsigned int	compute3DCentroid (ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &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 (ConstCloudIterator< PointT > &cloud_iterator, Eigen::Vector4f &centroid)
template<typename PointT >
unsigned int	compute3DCentroid (ConstCloudIterator< PointT > &cloud_iterator, Eigen::Vector4d &centroid)
template<typename PointT , typename Scalar >
unsigned int	compute3DCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 4, 1 > &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, Eigen::Vector4f &centroid)
template<typename PointT >
unsigned int	compute3DCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4d &centroid)
template<typename PointT , typename Scalar >
unsigned int	compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix< Scalar, 4, 1 > &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 std::vector< int > &indices, Eigen::Vector4f &centroid)
template<typename PointT >
unsigned int	compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Vector4d &centroid)
template<typename PointT , typename Scalar >
unsigned int	compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix< Scalar, 4, 1 > &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)
template<typename PointT >
unsigned int	compute3DCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Vector4d &centroid)
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 , typename Scalar >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &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 Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4d &centroid, Eigen::Matrix3d &covariance_matrix)
template<typename PointT , typename Scalar >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &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 std::vector< int > &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Vector4d &centroid, Eigen::Matrix3d &covariance_matrix)
template<typename PointT , typename Scalar >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &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)
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Vector4d &centroid, Eigen::Matrix3d &covariance_matrix)
template<typename PointT , typename Scalar >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &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::Matrix3f &covariance_matrix)
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix3d &covariance_matrix)
template<typename PointT , typename Scalar >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix< Scalar, 3, 3 > &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)
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix3d &covariance_matrix)
template<typename PointT , typename Scalar >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix< Scalar, 3, 3 > &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)
template<typename PointT >
unsigned int	computeCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix3d &covariance_matrix)
template<typename PointT , typename Scalar >
unsigned int	computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &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 Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
template<typename PointT >
unsigned int	computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4d &centroid, Eigen::Matrix3d &covariance_matrix)
template<typename PointT , typename Scalar >
unsigned int	computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &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 std::vector< int > &indices, const Eigen::Vector4f &centroid, Eigen::Matrix3f &covariance_matrix)
template<typename PointT >
unsigned int	computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, const Eigen::Vector4d &centroid, Eigen::Matrix3d &covariance_matrix)
template<typename PointT , typename Scalar >
unsigned int	computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &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)
template<typename PointT >
unsigned int	computeCovarianceMatrixNormalized (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, const Eigen::Vector4d &centroid, Eigen::Matrix3d &covariance_matrix)
template<typename PointT , typename Scalar >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &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::Matrix3f &covariance_matrix, Eigen::Vector4f &centroid)
template<typename PointT >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix3d &covariance_matrix, Eigen::Vector4d &centroid)
template<typename PointT , typename Scalar >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &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)
template<typename PointT >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix3d &covariance_matrix, Eigen::Vector4d &centroid)
template<typename PointT , typename Scalar >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &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)
template<typename PointT >
unsigned int	computeMeanAndCovarianceMatrix (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix3d &covariance_matrix, Eigen::Vector4d &centroid)
template<typename PointT , typename Scalar >
void	computeNDCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > &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, Eigen::VectorXf &centroid)
template<typename PointT >
void	computeNDCentroid (const pcl::PointCloud< PointT > &cloud, Eigen::VectorXd &centroid)
template<typename PointT , typename Scalar >
void	computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > &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)
template<typename PointT >
void	computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &indices, Eigen::VectorXd &centroid)
template<typename PointT , typename Scalar >
void	computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > &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)
template<typename PointT >
void	computeNDCentroid (const pcl::PointCloud< PointT > &cloud, const pcl::PointIndices &indices, Eigen::VectorXd &centroid)
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 pcl::PCLPointCloud2 &cloud1_in, const pcl::PCLPointCloud2 &cloud2_in, pcl::PCLPointCloud2 &cloud_out)
	Concatenate two datasets representing different fields.
PCL_EXPORTS bool	concatenatePointCloud (const pcl::PCLPointCloud2 &cloud1, const pcl::PCLPointCloud2 &cloud2, pcl::PCLPointCloud2 &cloud_out)
	Concatenate two pcl::PCLPointCloud2.
PCL_EXPORTS void	copyPointCloud (const pcl::PCLPointCloud2 &cloud_in, const std::vector< int > &indices, pcl::PCLPointCloud2 &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
PCL_EXPORTS void	copyPointCloud (const pcl::PCLPointCloud2 &cloud_in, const std::vector< int, Eigen::aligned_allocator< int > > &indices, pcl::PCLPointCloud2 &cloud_out)
	Extract the indices of a given point cloud as a new point cloud.
PCL_EXPORTS void	copyPointCloud (const pcl::PCLPointCloud2 &cloud_in, pcl::PCLPointCloud2 &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 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 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, pcl::PointCloud< PointOutT > &cloud_out)
	Copy all the fields from a given point cloud into 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 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 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, pcl::PCLPointCloud2 &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, pcl::PCLPointCloud2 &cloud, unsigned int point_index, unsigned int field_idx, unsigned int fields_count)
template<>
void	copyStringValue< uint8_t > (const std::string &st, pcl::PCLPointCloud2 &cloud, unsigned int point_index, unsigned int field_idx, unsigned int fields_count)
template<typename Type >
void	copyValueString (const pcl::PCLPointCloud2 &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 pcl::PCLPointCloud2 &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 pcl::PCLPointCloud2 &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< pcl::PCLPointField > &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 , typename Scalar >
void	demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Matrix< Scalar, 4, 1 > &centroid, pcl::PointCloud< PointT > &cloud_out, int npts=0)
	Subtract a centroid from a point cloud and return the de-meaned representation.
template<typename PointT >
void	demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Vector4f &centroid, pcl::PointCloud< PointT > &cloud_out, int npts=0)
template<typename PointT >
void	demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Vector4d &centroid, pcl::PointCloud< PointT > &cloud_out, int npts=0)
template<typename PointT , typename Scalar >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const Eigen::Matrix< Scalar, 4, 1 > &centroid, pcl::PointCloud< PointT > &cloud_out)
	Subtract a centroid from a point cloud and return the de-meaned representation.
template<typename PointT >
void	demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Vector4f &centroid, pcl::PointCloud< PointT > &cloud_out)
template<typename PointT >
void	demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Vector4d &centroid, pcl::PointCloud< PointT > &cloud_out)
template<typename PointT , typename Scalar >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, const Eigen::Matrix< Scalar, 4, 1 > &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)
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, const Eigen::Vector4d &centroid, pcl::PointCloud< PointT > &cloud_out)
template<typename PointT , typename Scalar >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, const Eigen::Matrix< Scalar, 4, 1 > &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 pcl::PointIndices &indices, const Eigen::Vector4f &centroid, pcl::PointCloud< PointT > &cloud_out)
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, const Eigen::Vector4d &centroid, pcl::PointCloud< PointT > &cloud_out)
template<typename PointT , typename Scalar >
void	demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > &cloud_out, int npts=0)
	Subtract a centroid from a point cloud and return the de-meaned representation as an Eigen matrix.
template<typename PointT >
void	demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Vector4f &centroid, Eigen::MatrixXf &cloud_out, int npts=0)
template<typename PointT >
void	demeanPointCloud (ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Vector4d &centroid, Eigen::MatrixXd &cloud_out, int npts=0)
template<typename PointT , typename Scalar >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > &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 Eigen::Vector4f &centroid, Eigen::MatrixXf &cloud_out)
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const Eigen::Vector4d &centroid, Eigen::MatrixXd &cloud_out)
template<typename PointT , typename Scalar >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > &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)
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, const Eigen::Vector4d &centroid, Eigen::MatrixXd &cloud_out)
template<typename PointT , typename Scalar >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, Eigen::Dynamic, Eigen::Dynamic > &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)
template<typename PointT >
void	demeanPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, const Eigen::Vector4d &centroid, Eigen::MatrixXd &cloud_out)
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 PointT >
double	estimateProjectionMatrix (typename pcl::PointCloud< PointT >::ConstPtr cloud, Eigen::Matrix< float, 3, 4, Eigen::RowMajor > &projection_matrix, const std::vector< int > &indices=std::vector< int >())
	Estimates the projection matrix P = K * (R|-R*t) from organized point clouds, with K = [[fx, s, cx], [0, fy, cy], [0, 0, 1]] R = rotation matrix and t = translation vector.
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< unsigned int >::max(), unsigned int max_label=std::numeric_limits< unsigned 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	fromPCLPointCloud2 (const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map)
	Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
template<typename PointT >
void	fromPCLPointCloud2 (const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud)
	Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object.
template<typename PointT >
void	fromROSMsg (const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map)
template<typename PointT >
void	fromROSMsg (const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud)
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.
PCL_EXPORTS void	getCameraMatrixFromProjectionMatrix (const Eigen::Matrix< float, 3, 4, Eigen::RowMajor > &projection_matrix, Eigen::Matrix3f &camera_matrix)
	Determines the camera matrix from the given projection matrix.
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, pcl::PCLPointCloud2 &out)
	Copy the XYZ dimensions from an Eigen MatrixXf into a pcl::PCLPointCloud2 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 pcl::PCLPointCloud2 &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< pcl::PCLPointField > &fields)
	Get the index of a specified field (i.e., dimension/channel)
template<typename PointT >
int	getFieldIndex (const std::string &field_name, std::vector< pcl::PCLPointField > &fields)
	Get the index of a specified field (i.e., dimension/channel)
template<typename PointT >
void	getFields (const pcl::PointCloud< PointT > &cloud, std::vector< pcl::PCLPointField > &fields)
	Get the list of available fields (i.e., dimension/channel)
template<typename PointT >
void	getFields (std::vector< pcl::PCLPointField > &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 pcl::PCLPointCloud2 &cloud)
	Get the available point cloud fields as a space separated string.
PCL_EXPORTS void	getFieldsSizes (const std::vector< pcl::PCLPointField > &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 PCLPointField from a specific size and type.
char	getFieldType (const int type)
	Obtains the type of the PCLPointField from a specific PCLPointField as a char.
template<typename PointT , typename ValT >
void	getFieldValue (const PointT &pt, size_t field_offset, ValT &value)
	Get the value at a specified field in a point.
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 pcl::PCLPointCloud2 &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 pcl::PCLPointCloud2ConstPtr &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 pcl::PCLPointCloud2ConstPtr &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.
template<typename PointT >
void	getMinMax3D (const typename pcl::PointCloud< PointT >::ConstPtr &cloud, const std::vector< int > &indices, 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 pcl::PCLPointCloud2 &in, Eigen::MatrixXf &out)
	Copy the XYZ dimensions of a pcl::PCLPointCloud2 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.
template<typename PointT , typename Scalar >
double	getPrincipalTransformation (const pcl::PointCloud< PointT > &cloud, Eigen::Transform< Scalar, 3, Eigen::Affine > &transform)
	Calculates the principal (PCA-based) alignment of the point cloud.
template<typename PointT >
double	getPrincipalTransformation (const pcl::PointCloud< PointT > &cloud, Eigen::Affine3f &transform)
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 ()
template<typename Scalar >
void	getTransformation (Scalar x, Scalar y, Scalar z, Scalar roll, Scalar pitch, Scalar yaw, Eigen::Transform< Scalar, 3, Eigen::Affine > &t)
	Create a transformation from the given translation and Euler angles (XYZ-convention)
void	getTransformation (float x, float y, float z, float roll, float pitch, float yaw, Eigen::Affine3f &t)
void	getTransformation (double x, double y, double z, double roll, double pitch, double yaw, Eigen::Affine3d &t)
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::Axis > (const pcl::Axis &)
template<>
bool	isFinite< pcl::BorderDescription > (const pcl::BorderDescription &p)
template<>
bool	isFinite< pcl::Boundary > (const pcl::Boundary &)
template<>
bool	isFinite< pcl::ESFSignature640 > (const pcl::ESFSignature640 &)
template<>
bool	isFinite< pcl::FPFHSignature33 > (const pcl::FPFHSignature33 &)
template<>
bool	isFinite< pcl::IntensityGradient > (const pcl::IntensityGradient &)
template<>
bool	isFinite< pcl::Label > (const pcl::Label &)
template<>
bool	isFinite< pcl::MomentInvariants > (const pcl::MomentInvariants &)
template<>
bool	isFinite< pcl::Normal > (const pcl::Normal &n)
template<>
bool	isFinite< pcl::NormalBasedSignature12 > (const pcl::NormalBasedSignature12 &)
template<>
bool	isFinite< pcl::PFHRGBSignature250 > (const pcl::PFHRGBSignature250 &)
template<>
bool	isFinite< pcl::PFHSignature125 > (const pcl::PFHSignature125 &)
template<>
bool	isFinite< pcl::PointXY > (const pcl::PointXY &p)
template<>
bool	isFinite< pcl::PPFRGBSignature > (const pcl::PPFRGBSignature &)
template<>
bool	isFinite< pcl::PPFSignature > (const pcl::PPFSignature &)
template<>
bool	isFinite< pcl::PrincipalCurvatures > (const pcl::PrincipalCurvatures &)
template<>
bool	isFinite< pcl::PrincipalRadiiRSD > (const pcl::PrincipalRadiiRSD &)
template<>
bool	isFinite< pcl::ReferenceFrame > (const pcl::ReferenceFrame &)
template<>
bool	isFinite< pcl::RGB > (const pcl::RGB &)
template<>
bool	isFinite< pcl::ShapeContext1980 > (const pcl::ShapeContext1980 &)
template<>
bool	isFinite< pcl::SHOT1344 > (const pcl::SHOT1344 &)
template<>
bool	isFinite< pcl::SHOT352 > (const pcl::SHOT352 &)
template<>
bool	isFinite< pcl::VFHSignature308 > (const pcl::VFHSignature308 &)
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 Point1T , typename Point2T >
bool	isSamePointType ()
	Check if two given point types are the same or not.
template<typename Type >
bool	isValueFinite (const pcl::PCLPointCloud2 &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 &s, const ::pcl::Vertices &v)
std::ostream &	operator<< (std::ostream &s, const ::pcl::PointIndices &v)
std::ostream &	operator<< (std::ostream &s, const ::pcl::ModelCoefficients &v)
std::ostream &	operator<< (std::ostream &out, const PCLHeader &h)
std::ostream &	operator<< (std::ostream &s, const ::pcl::PolygonMesh &v)
std::ostream &	operator<< (std::ostream &s, const ::pcl::PCLImage &v)
std::ostream &	operator<< (std::ostream &s, const ::pcl::PCLPointField &v)
std::ostream &	operator<< (std::ostream &s, const ::pcl::PCLPointCloud2 &v)
std::ostream &	operator<< (std::ostream &os, const RangeImageBorderExtractor::Parameters &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const Correspondence &c)
	overloaded << operator
std::ostream &	operator<< (std::ostream &os, const GradientXY &p)
template<typename real >
std::ostream &	operator<< (std::ostream &os, const BivariatePolynomialT< real > &p)
std::ostream &	operator<< (std::ostream &os, const _Axis &p)
std::ostream &	operator<< (std::ostream &os, const NarfKeypoint::Parameters &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXYZ &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const RGB &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const Intensity &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const Intensity8u &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const Intensity32u &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXYZI &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXYZL &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const Label &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXYZRGBA &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXYZRGB &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXYZRGBL &p)
template<typename PointT >
std::ostream &	operator<< (std::ostream &s, const pcl::PointCloud< PointT > &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXYZHSV &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXY &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointUV &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const InterestPoint &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const Normal &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const Axis &p)
std::ostream &	operator<< (std::ostream &os, const RangeImage &r)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointNormal &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXYZRGBNormal &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointXYZINormal &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointWithRange &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointWithViewpoint &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const MomentInvariants &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PrincipalRadiiRSD &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const Boundary &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PrincipalCurvatures &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PFHSignature125 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PFHRGBSignature250 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PPFSignature &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PPFRGBSignature &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const NormalBasedSignature12 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const ShapeContext1980 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const SHOT352 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const SHOT1344 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const ReferenceFrame &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const FPFHSignature33 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const VFHSignature308 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const ESFSignature640 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const GFPFHSignature16 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const Narf36 &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const BorderDescription &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const IntensityGradient &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointWithScale &p)
PCL_EXPORTS std::ostream &	operator<< (std::ostream &os, const PointSurfel &p)
template<int N>
std::ostream &	operator<< (std::ostream &os, const Histogram< N > &p)
	PCL_DEPRECATED (template< typename PointT > void fromROSMsg(const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map),"pcl::fromROSMsg is deprecated, please use fromPCLPointCloud2 instead.")
	Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
	PCL_DEPRECATED (template< typename PointT > void fromROSMsg(const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud),"pcl::fromROSMsg is deprecated, please use fromPCLPointCloud2 instead.")
	Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object.
	PCL_DEPRECATED (template< typename PointT > void toROSMsg(const pcl::PointCloud< PointT > &cloud, pcl::PCLPointCloud2 &msg),"pcl::fromROSMsg is deprecated, please use fromPCLPointCloud2 instead.")
	Convert a pcl::PointCloud<T> object to a PCLPointCloud2 binary data blob.
	PCL_DEPRECATED (template< typename CloudT > void toROSMsg(const CloudT &cloud, pcl::PCLImage &msg),"pcl::fromROSMsg is deprecated, please use fromPCLPointCloud2 instead.")
	Copy the RGB fields of a PointCloud into pcl::PCLImage format.
	PCL_DEPRECATED (inline void toROSMsg(const pcl::PCLPointCloud2 &cloud, pcl::PCLImage &msg),"pcl::fromROSMsg is deprecated, please use fromPCLPointCloud2 instead.")
	Copy the RGB fields of a PCLPointCloud2 msg into pcl::PCLImage format.
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.
void	PointCloudDepthAndRGBtoXYZRGBA (PointCloud< Intensity > &depth, PointCloud< RGB > &image, float &focal, PointCloud< PointXYZRGBA > &out)
	Convert registered Depth image and RGB image to PointCloudXYZRGBA.
void	PointCloudRGBtoI (PointCloud< RGB > &in, PointCloud< Intensity > &out)
	Convert a RGB point cloud to a Intensity.
void	PointCloudRGBtoI (PointCloud< RGB > &in, PointCloud< Intensity8u > &out)
	Convert a RGB point cloud to a Intensity.
void	PointCloudRGBtoI (PointCloud< RGB > &in, PointCloud< Intensity32u > &out)
	Convert a RGB point cloud to a Intensity.
void	PointCloudXYZRGBAtoXYZHSV (PointCloud< PointXYZRGBA > &in, PointCloud< PointXYZHSV > &out)
	Convert a XYZRGB point cloud to a XYZHSV.
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.
void	PointRGBtoI (RGB &in, Intensity &out)
	Convert a RGB point type to a I.
void	PointRGBtoI (RGB &in, Intensity8u &out)
	Convert a RGB point type to a I.
void	PointRGBtoI (RGB &in, Intensity32u &out)
	Convert a RGB point type to a I.
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)
void	PointXYZRGBAtoXYZHSV (PointXYZRGBA &in, PointXYZHSV &out)
	Convert a XYZRGB point type to a XYZHSV.
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<class Type >
void	read (std::istream &stream, Type &value)
	Function for reading data from a stream.
template<class Type >
void	read (std::istream &stream, Type *value, int nr_values)
	Function for reading data arrays from a stream.
template<typename NormalT >
bool	refineNormal (const PointCloud< NormalT > &cloud, int index, const std::vector< int > &k_indices, const std::vector< float > &k_sqr_distances, NormalT &point)
	Refine an indexed point based on its neighbors, this function only writes to the normal_* fields.
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.
template<typename PointT >
void	removeNaNNormalsFromPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, std::vector< int > &index)
	Removes points that have their normals invalid (i.e., 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.
void	seededHueSegmentation (const PointCloud< PointXYZRGB > &cloud, const boost::shared_ptr< search::Search< PointXYZRGB > > &tree, float tolerance, PointIndices &indices_in, PointIndices &indices_out, float delta_hue=0.0)
	Decompose a region of space into clusters based on the Euclidean distance between points.
void	seededHueSegmentation (const PointCloud< PointXYZRGB > &cloud, const boost::shared_ptr< search::Search< PointXYZRGBL > > &tree, float tolerance, PointIndices &indices_in, PointIndices &indices_out, float delta_hue=0.0)
	Decompose a region of space into clusters based on the Euclidean distance between points.
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.
template<typename PointT , typename ValT >
void	setFieldValue (PointT &pt, size_t field_offset, const ValT &value)
	Set the value at a specified field in a point.
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<>
float	squaredEuclideanDistance (const pcl::segmentation::grabcut::Color &c1, const pcl::segmentation::grabcut::Color &c2)
template<typename PointType1 , typename PointType2 >
float	squaredEuclideanDistance (const PointType1 &p1, const PointType2 &p2)
	Calculate the squared euclidean distance between the two given points.
template<>
float	squaredEuclideanDistance (const PointXY &p1, const PointXY &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.
template<typename PointT >
void	toPCLPointCloud2 (const pcl::PointCloud< PointT > &cloud, pcl::PCLPointCloud2 &msg)
	Convert a pcl::PointCloud<T> object to a PCLPointCloud2 binary data blob.
template<typename CloudT >
void	toPCLPointCloud2 (const CloudT &cloud, pcl::PCLImage &msg)
	Copy the RGB fields of a PointCloud into pcl::PCLImage format.
void	toPCLPointCloud2 (const pcl::PCLPointCloud2 &cloud, pcl::PCLImage &msg)
	Copy the RGB fields of a PCLPointCloud2 msg into pcl::PCLImage format.
template<typename PointT >
void	toROSMsg (const pcl::PointCloud< PointT > &cloud, pcl::PCLPointCloud2 &msg)
template<typename CloudT >
void	toROSMsg (const CloudT &cloud, pcl::PCLImage &msg)
void	toROSMsg (const pcl::PCLPointCloud2 &cloud, pcl::PCLImage &msg)
template<typename PointT , typename Scalar >
PointT	transformPoint (const PointT &point, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform)
	Transform a point with members x,y,z.
template<typename PointT >
PointT	transformPoint (const PointT &point, const Eigen::Affine3f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &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::Affine3f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &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)
template<typename PointT , typename Scalar >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform)
	Apply an affine transform defined by an Eigen Transform.
template<typename PointT >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Affine3f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform)
	Apply a rigid transform defined by a 4x4 matrix.
template<typename PointT >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix4f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform)
	Apply a rigid transform defined by a 4x4 matrix.
template<typename PointT >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix4f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform)
	Apply a rigid transform defined by a 4x4 matrix.
template<typename PointT >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix4f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 3, 1 > &offset, const Eigen::Quaternion< Scalar > &rotation)
	Apply a rigid transform defined by a 3D offset and a quaternion.
template<typename PointT >
void	transformPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Vector3f &offset, const Eigen::Quaternionf &rotation)
template<typename PointT , typename Scalar >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &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::Affine3f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform)
	Transform a point cloud and rotate its normals using an Eigen transform.
template<typename PointT >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Affine3f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform)
	Transform a point cloud and rotate its normals using an Eigen transform.
template<typename PointT >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Affine3f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &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)
template<typename PointT , typename Scalar >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform)
	Transform a point cloud and rotate its normals using an Eigen transform.
template<typename PointT >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const std::vector< int > &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix4f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform)
	Transform a point cloud and rotate its normals using an Eigen transform.
template<typename PointT >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, const pcl::PointIndices &indices, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix4f &transform)
template<typename PointT , typename Scalar >
void	transformPointCloudWithNormals (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 3, 1 > &offset, const Eigen::Quaternion< Scalar > &rotation)
	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)
template<typename Derived , typename OtherDerived >
Eigen::internal::umeyama_transform_matrix_type < Derived, OtherDerived > ::type	umeyama (const Eigen::MatrixBase< Derived > &src, const Eigen::MatrixBase< OtherDerived > &dst, bool with_scaling=false)
	Returns the transformation between two point sets. The algorithm is based on: "Least-squares estimation of transformation parameters between two point patterns", Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573.
template<class Type >
void	write (std::ostream &stream, Type value)
	Function for writing data to a stream.
template<class Type >
void	write (std::ostream &stream, Type *value, int nr_values)
	Function for writing data arrays to a stream.
Variables
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.

$Id$

---

Typedef Documentation

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

Definition at line 212 of file point_types.hpp.

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

Definition at line 213 of file point_types.hpp.

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

Definition at line 214 of file point_types.hpp.

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

Definition at line 215 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 pcl::PointCloud<pcl::PointXYZRGB> pcl::cc

Definition at line 95 of file visualization/src/cloud_viewer.cpp.

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

Definition at line 94 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 96 of file visualization/src/cloud_viewer.cpp.

typedef boost::shared_ptr<PCLHeader const> pcl::HeaderConstPtr

Definition at line 31 of file PCLHeader.h.

typedef boost::shared_ptr<PCLHeader> pcl::HeaderPtr

Definition at line 30 of file PCLHeader.h.

typedef std::vector<pcl::PointIndices> pcl::IndicesClusters

Definition at line 46 of file conditional_euclidean_clustering.h.

typedef boost::shared_ptr<std::vector<pcl::PointIndices> > pcl::IndicesClustersPtr

Definition at line 47 of file conditional_euclidean_clustering.h.

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

Definition at line 61 of file pcl_base.h.

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

Definition at line 60 of file pcl_base.h.

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

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

typedef boost::shared_ptr< ::pcl::ModelCoefficients const> pcl::ModelCoefficientsConstPtr

Definition at line 28 of file ModelCoefficients.h.

typedef boost::shared_ptr< ::pcl::ModelCoefficients> pcl::ModelCoefficientsPtr

Definition at line 27 of file ModelCoefficients.h.

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

Definition at line 65 of file point_cloud.h.

typedef boost::shared_ptr< ::pcl::PCLImage const> pcl::PCLImageConstPtr

Definition at line 38 of file PCLImage.h.

typedef boost::shared_ptr< ::pcl::PCLImage> pcl::PCLImagePtr

Definition at line 37 of file PCLImage.h.

typedef boost::shared_ptr< ::pcl::PCLPointCloud2 const> pcl::PCLPointCloud2ConstPtr

Definition at line 56 of file PCLPointCloud2.h.

typedef boost::shared_ptr< ::pcl::PCLPointCloud2> pcl::PCLPointCloud2Ptr

Definition at line 55 of file PCLPointCloud2.h.

typedef boost::shared_ptr< ::pcl::PCLPointField const> pcl::PCLPointFieldConstPtr

Definition at line 42 of file PCLPointField.h.

typedef boost::shared_ptr< ::pcl::PCLPointField> pcl::PCLPointFieldPtr

Definition at line 41 of file PCLPointField.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 boost::shared_ptr< ::pcl::PointIndices const> pcl::PointIndicesConstPtr

Definition at line 27 of file PointIndices.h.

typedef boost::shared_ptr< ::pcl::PointIndices> pcl::PointIndicesPtr

Definition at line 26 of file PointIndices.h.

typedef boost::shared_ptr< ::pcl::PolygonMesh const> pcl::PolygonMeshConstPtr

Definition at line 33 of file PolygonMesh.h.

typedef boost::shared_ptr< ::pcl::PolygonMesh> pcl::PolygonMeshPtr

Definition at line 32 of file PolygonMesh.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 110 of file TextureMesh.h.

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

Definition at line 109 of file TextureMesh.h.

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

Definition at line 216 of file point_types.hpp.

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

Definition at line 217 of file point_types.hpp.

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

Definition at line 218 of file point_types.hpp.

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

Definition at line 219 of file point_types.hpp.

typedef VectorAverage<float, 2> pcl::VectorAverage2f

Definition at line 111 of file vector_average.h.

typedef VectorAverage<float, 3> pcl::VectorAverage3f

Definition at line 112 of file vector_average.h.

typedef VectorAverage<float, 4> pcl::VectorAverage4f

Definition at line 113 of file vector_average.h.

typedef boost::shared_ptr<Vertices const> pcl::VerticesConstPtr

Definition at line 27 of file Vertices.h.

typedef boost::shared_ptr<Vertices> pcl::VerticesPtr

Definition at line 26 of file Vertices.h.

---

Enumeration Type Documentation

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_REGISTRATION_2D
SACMODEL_PARALLEL_PLANE
SACMODEL_NORMAL_PARALLEL_PLANE
SACMODEL_STICK

Definition at line 48 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 NormalT >

std::vector<float> pcl::assignNormalWeights	(	const PointCloud< NormalT > &	,
		int	,
		const std::vector< int > &	k_indices,
		const std::vector< float > &	k_sqr_distances
	)		[inline]

Assign weights of nearby normals used for refinement.

Todo:

Currently, this function equalizes all weights to 1

Parameters:

cloud	the point cloud data
index	a valid index in cloud representing a valid (i.e., finite) query point
k_indices	indices of neighboring points
k_sqr_distances	squared distances to the neighboring points

Returns:

weights

Definition at line 55 of file normal_refinement.h.

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::comparePair	(	std::pair< float, int >	i,
		std::pair< float, int >	j
	)		[inline]

This function is used as a comparator for sorting.

Definition at line 342 of file region_growing.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	(	ConstCloudIterator< PointT > &	cloud_iterator,
		Eigen::Vector4f &	centroid
	)		[inline]

Definition at line 68 of file centroid.h.

template<typename PointT >

unsigned int pcl::compute3DCentroid	(	ConstCloudIterator< PointT > &	cloud_iterator,
		Eigen::Vector4d &	centroid
	)		[inline]

Definition at line 75 of file centroid.h.

template<typename PointT >

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

Definition at line 93 of file centroid.h.

template<typename PointT >

unsigned int pcl::compute3DCentroid	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Vector4d &	centroid
	)		[inline]

Definition at line 100 of file centroid.h.

template<typename PointT >

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

Definition at line 121 of file centroid.h.

template<typename PointT >

unsigned int pcl::compute3DCentroid	(	const pcl::PointCloud< PointT > &	cloud,
		const std::vector< int > &	indices,
		Eigen::Vector4d &	centroid
	)		[inline]

Definition at line 129 of file centroid.h.

template<typename PointT >

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

Definition at line 151 of file centroid.h.

template<typename PointT >

unsigned int pcl::compute3DCentroid	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::PointIndices &	indices,
		Eigen::Vector4d &	centroid
	)		[inline]

Definition at line 159 of file centroid.h.

template<typename PointT >

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

Definition at line 185 of file centroid.h.

template<typename PointT >

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

Definition at line 193 of file centroid.h.

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]

Definition at line 254 of file centroid.h.

template<typename PointT >

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

Definition at line 263 of file centroid.h.

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]

Definition at line 291 of file centroid.h.

template<typename PointT >

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

Definition at line 300 of file centroid.h.

template<typename PointT >

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

Definition at line 508 of file centroid.h.

template<typename PointT >

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

Definition at line 515 of file centroid.h.

template<typename PointT >

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

Definition at line 539 of file centroid.h.

template<typename PointT >

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

Definition at line 547 of file centroid.h.

template<typename PointT >

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

Definition at line 572 of file centroid.h.

template<typename PointT >

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

Definition at line 580 of file centroid.h.

template<typename PointT >

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

Definition at line 219 of file centroid.h.

template<typename PointT >

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

Definition at line 227 of file centroid.h.

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]

Definition at line 330 of file centroid.h.

template<typename PointT >

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

Definition at line 339 of file centroid.h.

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]

Definition at line 368 of file centroid.h.

template<typename PointT >

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

Definition at line 377 of file centroid.h.

template<typename PointT >

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

Definition at line 403 of file centroid.h.

template<typename PointT >

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

Definition at line 411 of file centroid.h.

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]

Definition at line 438 of file centroid.h.

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]

Definition at line 447 of file centroid.h.

template<typename PointT >

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

Definition at line 475 of file centroid.h.

template<typename PointT >

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

Definition at line 484 of file centroid.h.

template<typename PointT >

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

Definition at line 879 of file centroid.h.

template<typename PointT >

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

Definition at line 886 of file centroid.h.

template<typename PointT >

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

Definition at line 905 of file centroid.h.

template<typename PointT >

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

Definition at line 913 of file centroid.h.

template<typename PointT >

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

Definition at line 933 of file centroid.h.

template<typename PointT >

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

Definition at line 941 of file centroid.h.

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 45 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 60 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 92 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 44 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 107 of file pfh.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 92 of file common/include/pcl/common/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 74 of file common/include/pcl/common/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 49 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 150 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 Type >

void pcl::copyStringValue	(	const std::string &	st,
		pcl::PCLPointCloud2 &	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,
		pcl::PCLPointCloud2 &	cloud,
		unsigned int	point_index,
		unsigned int	field_idx,
		unsigned int	fields_count
	)		[inline]

Definition at line 340 of file io/include/pcl/io/file_io.h.

template<>

void pcl::copyStringValue< uint8_t >	(	const std::string &	st,
		pcl::PCLPointCloud2 &	cloud,
		unsigned int	point_index,
		unsigned int	field_idx,
		unsigned int	fields_count
	)		[inline]

Definition at line 366 of file io/include/pcl/io/file_io.h.

template<typename Type >

void pcl::copyValueString	(	const pcl::PCLPointCloud2 &	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 pcl::PCLPointCloud2 &	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 pcl::PCLPointCloud2 &	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< pcl::PCLPointField > &	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 123 of file conversions.h.

template<typename PointT >

void pcl::demeanPointCloud	(	ConstCloudIterator< PointT > &	cloud_iterator,
		const Eigen::Vector4f &	centroid,
		pcl::PointCloud< PointT > &	cloud_out,
		int	npts = 0
	)

Definition at line 601 of file centroid.h.

template<typename PointT >

void pcl::demeanPointCloud	(	ConstCloudIterator< PointT > &	cloud_iterator,
		const Eigen::Vector4d &	centroid,
		pcl::PointCloud< PointT > &	cloud_out,
		int	npts = 0
	)

Definition at line 610 of file centroid.h.

template<typename PointT >

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

Definition at line 630 of file centroid.h.

template<typename PointT >

void pcl::demeanPointCloud	(	ConstCloudIterator< PointT > &	cloud_iterator,
		const Eigen::Vector4d &	centroid,
		pcl::PointCloud< PointT > &	cloud_out
	)

Definition at line 638 of file centroid.h.

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
	)

Definition at line 659 of file centroid.h.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const std::vector< int > &	indices,
		const Eigen::Vector4d &	centroid,
		pcl::PointCloud< PointT > &	cloud_out
	)

Definition at line 668 of file centroid.h.

template<typename PointT >

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

Definition at line 690 of file centroid.h.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const pcl::PointIndices &	indices,
		const Eigen::Vector4d &	centroid,
		pcl::PointCloud< PointT > &	cloud_out
	)

Definition at line 699 of file centroid.h.

template<typename PointT >

void pcl::demeanPointCloud	(	ConstCloudIterator< PointT > &	cloud_iterator,
		const Eigen::Vector4f &	centroid,
		Eigen::MatrixXf &	cloud_out,
		int	npts = 0
	)

Definition at line 723 of file centroid.h.

template<typename PointT >

void pcl::demeanPointCloud	(	ConstCloudIterator< PointT > &	cloud_iterator,
		const Eigen::Vector4d &	centroid,
		Eigen::MatrixXd &	cloud_out,
		int	npts = 0
	)

Definition at line 732 of file centroid.h.

template<typename PointT >

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

Definition at line 754 of file centroid.h.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const Eigen::Vector4d &	centroid,
		Eigen::MatrixXd &	cloud_out
	)

Definition at line 762 of file centroid.h.

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
	)

Definition at line 785 of file centroid.h.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const std::vector< int > &	indices,
		const Eigen::Vector4d &	centroid,
		Eigen::MatrixXd &	cloud_out
	)

Definition at line 794 of file centroid.h.

template<typename PointT >

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

Definition at line 818 of file centroid.h.

template<typename PointT >

void pcl::demeanPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		const pcl::PointIndices &	indices,
		const Eigen::Vector4d &	centroid,
		Eigen::MatrixXd &	cloud_out
	)

Definition at line 827 of file centroid.h.

template<typename Matrix >

Matrix::Scalar pcl::determinant3x3Matrix

(

const Matrix &

matrix

)

[inline]

Definition at line 650 of file common/include/pcl/common/eigen.h.

template<typename PointT >

double pcl::estimateProjectionMatrix	(	typename pcl::PointCloud< PointT >::ConstPtr	cloud,
		Eigen::Matrix< float, 3, 4, Eigen::RowMajor > &	projection_matrix,
		const std::vector< int > &	indices = std::vector<int> ()
	)

Estimates the projection matrix P = K * (R|-R*t) from organized point clouds, with K = [[fx, s, cx], [0, fy, cy], [0, 0, 1]] R = rotation matrix and t = translation vector.

Parameters:

[in]	cloud	input cloud. Must be organized and from a projective device. e.g. stereo or kinect, ...
[out]	projection_matrix	output projection matrix
[in]	indices	The indices to be used to determine the projection matrix

Returns:

the resudial error. A high residual indicates, that the point cloud was not from a projective device.

Definition at line 78 of file projection_matrix.hpp.

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 196 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 118 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<unsigned int>::max (),
		unsigned int	max_label = std::numeric_limits<unsigned 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 119 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 146 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 167 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::fromPCLPointCloud2	(	const pcl::PCLPointCloud2 &	msg,
		pcl::PointCloud< PointT > &	cloud,
		const MsgFieldMap &	field_map
	)

Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.

Parameters:

[in]	msg	the PCLPointCloud2 binary blob
[out]	cloud	the resultant pcl::PointCloud<T>
[in]	field_map	a MsgFieldMap object

Note:

Use fromPCLPointCloud2 (PCLPointCloud2, PointCloud<T>) directly or create you own MsgFieldMap using:

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

Definition at line 167 of file conversions.h.

template<typename PointT >

void pcl::fromPCLPointCloud2	(	const pcl::PCLPointCloud2 &	msg,
		pcl::PointCloud< PointT > &	cloud
	)

Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object.

Parameters:

[in]	msg	the PCLPointCloud2 binary blob
[out]	cloud	the resultant pcl::PointCloud<T>

Definition at line 225 of file conversions.h.

template<typename PointT >

void pcl::fromROSMsg	(	const pcl::PCLPointCloud2 &	msg,
		pcl::PointCloud< PointT > &	cloud,
		const MsgFieldMap &	field_map
	)

Definition at line 71 of file ros/conversions.h.

template<typename PointT >

void pcl::fromROSMsg	(	const pcl::PCLPointCloud2 &	msg,
		pcl::PointCloud< PointT > &	cloud
	)

Definition at line 85 of file ros/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 122 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 46 of file file_io.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.

void pcl::getCameraMatrixFromProjectionMatrix	(	const Eigen::Matrix< float, 3, 4, Eigen::RowMajor > &	projection_matrix,
		Eigen::Matrix3f &	camera_matrix
	)

Determines the camera matrix from the given projection matrix.

Note:

This method does NOT use a RQ decomposition, but uses the fact that the left 3x3 matrix P' of P squared eliminates the rotational part. P' = K * R -> P' * P'^T = K * R * R^T * K = K * K^T

Parameters:

[in]	projection_matrix
[out]	camera_matrix

Definition at line 42 of file projection_matrix.cpp.

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 56 of file rsd.h.

PCL_EXPORTS void pcl::getFieldsSizes	(	const std::vector< pcl::PCLPointField > &	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

template<typename PointT , typename ValT >

void pcl::getFieldValue	(	const PointT &	pt,
		size_t	field_offset,
		ValT &	value
	)		[inline]

Get the value at a specified field in a point.

Parameters:

[in]	pt	the point to get the value from
[in]	field_offset	the offset of the field
[out]	value	the value to retreive

Definition at line 297 of file point_traits.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 81 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 75 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 69 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 85 of file voxel_grid.h.

void pcl::getMinMax3D	(	const pcl::PCLPointCloud2ConstPtr &	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 pcl::PCLPointCloud2 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 49 of file filters/src/voxel_grid.cpp.

void pcl::getMinMax3D	(	const pcl::PCLPointCloud2ConstPtr &	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 pcl::PCLPointCloud2 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 94 of file filters/src/voxel_grid.cpp.

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 47 of file voxel_grid.hpp.

template<typename PointT >

void pcl::getMinMax3D	(	const typename pcl::PointCloud< PointT >::ConstPtr &	cloud,
		const std::vector< int > &	indices,
		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]	indices	the vector of indices to use
[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 125 of file voxel_grid.hpp.

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 , typename Scalar >

double pcl::getPrincipalTransformation	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Transform< Scalar, 3, Eigen::Affine > &	transform
	)		[inline]

Calculates the principal (PCA-based) alignment of the point cloud.

Parameters:

[in]	cloud	the input point cloud
[out]	transform	the resultant transform

Returns:

the ratio lambda1/lambda2 or lambda2/lambda3, whatever is closer to 1.

Note:

If the return value is close to one then the transformation might be not unique -> two principal directions have almost same variance (extend)

Definition at line 309 of file transforms.hpp.

template<typename PointT >

double pcl::getPrincipalTransformation	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Affine3f &	transform
	)		[inline]

Definition at line 411 of file common/include/pcl/common/transforms.h.

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 45 of file correspondence.cpp.

double pcl::getTime

(

)

[inline]

Definition at line 146 of file common/time.h.

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

Definition at line 781 of file common/include/pcl/common/eigen.h.

void pcl::getTransformation	(	double	x,
		double	y,
		double	z,
		double	roll,
		double	pitch,
		double	yaw,
		Eigen::Affine3d &	t
	)		[inline]

Definition at line 788 of file common/include/pcl/common/eigen.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 53 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::Axis >

(

const pcl::Axis &

)

[inline]

Definition at line 68 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::BorderDescription >

(

const pcl::BorderDescription &

p

)

[inline]

Definition at line 96 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::Boundary >

(

const pcl::Boundary &

)

[inline]

Definition at line 71 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::ESFSignature640 >

(

const pcl::ESFSignature640 &

)

[inline]

Definition at line 84 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::FPFHSignature33 >

(

const pcl::FPFHSignature33 &

)

[inline]

Definition at line 82 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::IntensityGradient >

(

const pcl::IntensityGradient &

)

[inline]

Definition at line 85 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::Label >

(

const pcl::Label &

)

[inline]

Definition at line 67 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::MomentInvariants >

(

const pcl::MomentInvariants &

)

[inline]

Definition at line 69 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::Normal >

(

const pcl::Normal &

n

)

[inline]

Definition at line 103 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::NormalBasedSignature12 >

(

const pcl::NormalBasedSignature12 &

)

[inline]

Definition at line 81 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::PFHRGBSignature250 >

(

const pcl::PFHRGBSignature250 &

)

[inline]

Definition at line 78 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::PFHSignature125 >

(

const pcl::PFHSignature125 &

)

[inline]

Definition at line 77 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::PointXY >

(

const pcl::PointXY &

p

)

[inline]

Definition at line 89 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::PPFRGBSignature >

(

const pcl::PPFRGBSignature &

)

[inline]

Definition at line 80 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::PPFSignature >

(

const pcl::PPFSignature &

)

[inline]

Definition at line 79 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::PrincipalCurvatures >

(

const pcl::PrincipalCurvatures &

)

[inline]

Definition at line 72 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::PrincipalRadiiRSD >

(

const pcl::PrincipalRadiiRSD &

)

[inline]

Definition at line 70 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::ReferenceFrame >

(

const pcl::ReferenceFrame &

)

[inline]

Definition at line 75 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::RGB >

(

const pcl::RGB &

)

[inline]

Definition at line 66 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::ShapeContext1980 >

(

const pcl::ShapeContext1980 &

)

[inline]

Definition at line 76 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::SHOT1344 >

(

const pcl::SHOT1344 &

)

[inline]

Definition at line 74 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::SHOT352 >

(

const pcl::SHOT352 &

)

[inline]

Definition at line 73 of file point_tests.h.

template<>

bool pcl::isFinite< pcl::VFHSignature308 >

(

const pcl::VFHSignature308 &

)

[inline]

Definition at line 83 of file point_tests.h.

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 Point1T , typename Point2T >

bool pcl::isSamePointType

(

)

[inline]

Check if two given point types are the same or not.

Definition at line 273 of file common/include/pcl/common/io.h.

template<typename Type >

bool pcl::isValueFinite	(	const pcl::PCLPointCloud2 &	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 68 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.

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.

std::ostream& pcl::operator<<	(	std::ostream &	s,
		const ::pcl::Vertices &	v
	)		[inline]

Definition at line 29 of file Vertices.h.

std::ostream& pcl::operator<<	(	std::ostream &	s,
		const ::pcl::PointIndices &	v
	)		[inline]

Definition at line 29 of file PointIndices.h.

std::ostream& pcl::operator<<	(	std::ostream &	s,
		const ::pcl::ModelCoefficients &	v
	)		[inline]

Definition at line 30 of file ModelCoefficients.h.

std::ostream& pcl::operator<<	(	std::ostream &	out,
		const PCLHeader &	h
	)		[inline]

Definition at line 33 of file PCLHeader.h.

std::ostream& pcl::operator<<	(	std::ostream &	s,
		const ::pcl::PolygonMesh &	v
	)		[inline]

Definition at line 35 of file PolygonMesh.h.

std::ostream& pcl::operator<<	(	std::ostream &	s,
		const ::pcl::PCLImage &	v
	)		[inline]

Definition at line 40 of file PCLImage.h.

std::ostream& pcl::operator<<	(	std::ostream &	s,
		const ::pcl::PCLPointField &	v
	)		[inline]

Definition at line 44 of file PCLPointField.h.

std::ostream& pcl::operator<<	(	std::ostream &	s,
		const ::pcl::PCLPointCloud2 &	v
	)		[inline]

Definition at line 58 of file PCLPointCloud2.h.

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

Definition at line 60 of file range_image_border_extractor.hpp.

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

overloaded << operator

Definition at line 88 of file correspondence.cpp.

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

Definition at line 107 of file color_gradient_dot_modality.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 _Axis &	p
	)

Definition at line 165 of file point_types.cpp.

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

Definition at line 197 of file narf_keypoint.h.

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

Definition at line 42 of file point_types.cpp.

std::ostream & pcl::operator<<	(	std::ostream &	os,
		const RGB &	p
	)

Definition at line 49 of file point_types.cpp.

std::ostream & pcl::operator<<	(	std::ostream &	os,
		const Intensity &	p
	)

Definition at line 60 of file point_types.cpp.

std::ostream & pcl::operator<<	(	std::ostream &	os,
		const Intensity8u &	p
	)

Definition at line 67 of file point_types.cpp.

PCL_EXPORTS std::ostream& pcl::operator<<	(	std::ostream &	os,
		const Intensity32u &	p
	)

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

Definition at line 74 of file point_types.cpp.

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

Definition at line 81 of file point_types.cpp.

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

Definition at line 88 of file point_types.cpp.

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

Definition at line 95 of file point_types.cpp.

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

Definition at line 106 of file point_types.cpp.

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

Definition at line 116 of file point_types.cpp.

template<typename PointT >

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

Definition at line 602 of file point_cloud.h.

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

Definition at line 123 of file point_types.cpp.

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

Definition at line 130 of file point_types.cpp.

std::ostream & pcl::operator<<	(	std::ostream &	os,
		const PointUV &	p
	)

Definition at line 137 of file point_types.cpp.

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

Definition at line 144 of file point_types.cpp.

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

Definition at line 151 of file point_types.cpp.

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

Definition at line 158 of file point_types.cpp.

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

/ingroup range_image

Definition at line 817 of file range_image.h.

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

Definition at line 172 of file point_types.cpp.

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

Definition at line 179 of file point_types.cpp.

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

Definition at line 186 of file point_types.cpp.

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

Definition at line 193 of file point_types.cpp.

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

Definition at line 200 of file point_types.cpp.

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

Definition at line 207 of file point_types.cpp.

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

Definition at line 214 of file point_types.cpp.

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

Definition at line 221 of file point_types.cpp.

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

Definition at line 228 of file point_types.cpp.

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

Definition at line 235 of file point_types.cpp.

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

Definition at line 243 of file point_types.cpp.

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

Definition at line 251 of file point_types.cpp.

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

Definition at line 258 of file point_types.cpp.

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

Definition at line 266 of file point_types.cpp.

std::ostream & pcl::operator<<	(	std::ostream &	os,
		const ShapeContext1980 &	p
	)

Definition at line 274 of file point_types.cpp.

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

Definition at line 284 of file point_types.cpp.

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

Definition at line 294 of file point_types.cpp.

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

Definition at line 304 of file point_types.cpp.

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

Definition at line 314 of file point_types.cpp.

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

Definition at line 322 of file point_types.cpp.

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

Definition at line 330 of file point_types.cpp.

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

Definition at line 338 of file point_types.cpp.

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

Definition at line 346 of file point_types.cpp.

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

Definition at line 355 of file point_types.cpp.

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

Definition at line 362 of file point_types.cpp.

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

Definition at line 369 of file point_types.cpp.

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

Definition at line 376 of file point_types.cpp.

template<int N>

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

Definition at line 1484 of file point_types.hpp.

pcl::PCL_DEPRECATED	(	template< typename PointT > void	fromROSMsgconst pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map,
		"pcl::fromROSMsg is	deprecated,
		please use fromPCLPointCloud2 instead."
	)

Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.

Parameters:

[in]	msg	the PCLPointCloud2 binary blob
[out]	cloud	the resultant pcl::PointCloud<T>
[in]	field_map	a MsgFieldMap object

Note:

Use fromROSMsg (PCLPointCloud2, PointCloud<T>) directly or create you own MsgFieldMap using:

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

pcl::PCL_DEPRECATED	(	template< typename PointT > void	fromROSMsgconst pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud,
		"pcl::fromROSMsg is	deprecated,
		please use fromPCLPointCloud2 instead."
	)

Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object.

Parameters:

[in]	msg	the PCLPointCloud2 binary blob
[out]	cloud	the resultant pcl::PointCloud<T>

pcl::PCL_DEPRECATED	(	template< typename PointT > void	toROSMsgconst pcl::PointCloud< PointT > &cloud, pcl::PCLPointCloud2 &msg,
		"pcl::fromROSMsg is	deprecated,
		please use fromPCLPointCloud2 instead."
	)

Convert a pcl::PointCloud<T> object to a PCLPointCloud2 binary data blob.

Parameters:

[in]	cloud	the input pcl::PointCloud<T>
[out]	msg	the resultant PCLPointCloud2 binary blob

pcl::PCL_DEPRECATED	(	template< typename CloudT > void	toROSMsgconst CloudT &cloud, pcl::PCLImage &msg,
		"pcl::fromROSMsg is	deprecated,
		please use fromPCLPointCloud2 instead."
	)

Copy the RGB fields of a PointCloud into pcl::PCLImage format.

Parameters:

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

Note:

will throw std::runtime_error if there is a problem

pcl::PCL_DEPRECATED	(	inline void	toROSMsgconst pcl::PCLPointCloud2 &cloud, pcl::PCLImage &msg,
		"pcl::fromROSMsg is	deprecated,
		please use fromPCLPointCloud2 instead."
	)

Copy the RGB fields of a PCLPointCloud2 msg into pcl::PCLImage format.

Parameters:

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

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.

Note:

Described in: "Intersection of Two Planes, John Krumm, Microsoft Research, Redmond, WA, USA"

Parameters:

[in]	plane_a	coefficients of plane A and plane B in the form ax + by + cz + d = 0
[out]	plane_b	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 71 of file intersections.cpp.

void pcl::PointCloudDepthAndRGBtoXYZRGBA	(	PointCloud< Intensity > &	depth,
		PointCloud< RGB > &	image,
		float &	focal,
		PointCloud< PointXYZRGBA > &	out
	)

Convert registered Depth image and RGB image to PointCloudXYZRGBA.

Parameters:

[in]	depth	the input depth image as intensity points in float
[in]	image	the input RGB image
[in]	focal	the focal length
[out]	out	the output pointcloud

Definition at line 354 of file point_types_conversion.h.

void pcl::PointCloudRGBtoI	(	PointCloud< RGB > &	in,
		PointCloud< Intensity > &	out
	)		[inline]

Convert a RGB point cloud to a Intensity.

Parameters:

[in]	in	the input RGB point cloud
[out]	out	the output Intensity point cloud

Definition at line 244 of file point_types_conversion.h.

void pcl::PointCloudRGBtoI	(	PointCloud< RGB > &	in,
		PointCloud< Intensity8u > &	out
	)		[inline]

Convert a RGB point cloud to a Intensity.

Parameters:

[in]	in	the input RGB point cloud
[out]	out	the output Intensity point cloud

Definition at line 262 of file point_types_conversion.h.

void pcl::PointCloudRGBtoI	(	PointCloud< RGB > &	in,
		PointCloud< Intensity32u > &	out
	)		[inline]

Convert a RGB point cloud to a Intensity.

Parameters:

[in]	in	the input RGB point cloud
[out]	out	the output Intensity point cloud

Definition at line 280 of file point_types_conversion.h.

void pcl::PointCloudXYZRGBAtoXYZHSV	(	PointCloud< PointXYZRGBA > &	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 316 of file point_types_conversion.h.

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 298 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 334 of file point_types_conversion.h.

void pcl::PointRGBtoI	(	RGB &	in,
		Intensity &	out
	)		[inline]

Convert a RGB point type to a I.

Parameters:

[in]	in	the input RGB point
[out]	out	the output Intensity point

Definition at line 68 of file point_types_conversion.h.

void pcl::PointRGBtoI	(	RGB &	in,
		Intensity8u &	out
	)		[inline]

Convert a RGB point type to a I.

Parameters:

[in]	in	the input RGB point
[out]	out	the output Intensity point

Definition at line 79 of file point_types_conversion.h.

void pcl::PointRGBtoI	(	RGB &	in,
		Intensity32u &	out
	)		[inline]

Convert a RGB point type to a I.

Parameters:

[in]	in	the input RGB point
[out]	out	the output Intensity point

Definition at line 91 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 108 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 119 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 83 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 94 of file sac_model_plane.h.

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

Definition at line 176 of file point_types_conversion.h.

void pcl::PointXYZRGBAtoXYZHSV	(	PointXYZRGBA &	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

Todo:

include the A parameter but how?

Definition at line 140 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 103 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 56 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 56 of file sac_model_plane.h.

template<class Type >

void pcl::read	(	std::istream &	stream,
		Type &	value
	)

Function for reading data from a stream.

Definition at line 47 of file region_xy.h.

template<class Type >

void pcl::read	(	std::istream &	stream,
		Type *	value,
		int	nr_values
	)

Function for reading data arrays from a stream.

Definition at line 54 of file region_xy.h.

template<typename NormalT >

bool pcl::refineNormal	(	const PointCloud< NormalT > &	cloud,
		int	index,
		const std::vector< int > &	k_indices,
		const std::vector< float > &	k_sqr_distances,
		NormalT &	point
	)		[inline]

Refine an indexed point based on its neighbors, this function only writes to the normal_* fields.

Note:

If the indexed point has only NaNs in its neighborhood, the resulting normal will be zero.

Parameters:

cloud	the point cloud data
index	a valid index in cloud representing a valid (i.e., finite) query point
k_indices	indices of neighboring points
k_sqr_distances	squared distances to the neighboring points
point	the output point, only normal_* fields are written

Returns:

false if an error occurred (norm of summed normals zero or all neighbors NaN)

Definition at line 81 of file normal_refinement.h.

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 46 of file filter.hpp.

template<typename PointT >

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

Removes points that have their normals invalid (i.e., 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 97 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]

void pcl::seededHueSegmentation	(	const PointCloud< PointXYZRGB > &	cloud,
		const boost::shared_ptr< search::Search< PointXYZRGB > > &	tree,
		float	tolerance,
		PointIndices &	indices_in,
		PointIndices &	indices_out,
		float	delta_hue = 0.0
	)

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
[in]	indices_in	the cluster containing the seed point indices (as a vector of PointIndices)
[out]	indices_out
[in]	delta_hue

Todo:

look how to make this templated!

Definition at line 46 of file seeded_hue_segmentation.hpp.

void pcl::seededHueSegmentation	(	const PointCloud< PointXYZRGB > &	cloud,
		const boost::shared_ptr< search::Search< PointXYZRGBL > > &	tree,
		float	tolerance,
		PointIndices &	indices_in,
		PointIndices &	indices_out,
		float	delta_hue = 0.0
	)

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
[in]	indices_in	the cluster containing the seed point indices (as a vector of PointIndices)
[out]	indices_out
[in]	delta_hue

Todo:

look how to make this templated!

Definition at line 122 of file seeded_hue_segmentation.hpp.

template<typename PointT , typename ValT >

void pcl::setFieldValue	(	PointT &	pt,
		size_t	field_offset,
		const ValT &	value
	)		[inline]

Set the value at a specified field in a point.

Parameters:

[out]	pt	the point to set the value to
[in]	field_offset	the offset of the field
[in]	value	the value to set

Definition at line 285 of file point_traits.h.

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 48 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 61 of file feature.hpp.

template<>

float pcl::squaredEuclideanDistance	(	const pcl::segmentation::grabcut::Color &	c1,
		const pcl::segmentation::grabcut::Color &	c2
	)

Definition at line 15 of file grabcut.hpp.

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<>

float pcl::squaredEuclideanDistance	(	const PointXY &	p1,
		const PointXY &	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 185 of file common/include/pcl/common/distances.h.

template<typename PointT >

void pcl::toPCLPointCloud2	(	const pcl::PointCloud< PointT > &	cloud,
		pcl::PCLPointCloud2 &	msg
	)

Convert a pcl::PointCloud<T> object to a PCLPointCloud2 binary data blob.

Parameters:

[in]	cloud	the input pcl::PointCloud<T>
[out]	msg	the resultant PCLPointCloud2 binary blob

Todo:

msg.is_bigendian = ?;

Definition at line 237 of file conversions.h.

template<typename CloudT >

void pcl::toPCLPointCloud2	(	const CloudT &	cloud,
		pcl::PCLImage &	msg
	)

Copy the RGB fields of a PointCloud into pcl::PCLImage format.

Parameters:

[in]	cloud	the point cloud message
[out]	msg	the resultant pcl::PCLImage 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 275 of file conversions.h.

void pcl::toPCLPointCloud2	(	const pcl::PCLPointCloud2 &	cloud,
		pcl::PCLImage &	msg
	)		[inline]

Copy the RGB fields of a PCLPointCloud2 msg into pcl::PCLImage format.

Parameters:

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

Definition at line 308 of file conversions.h.

template<typename PointT >

void pcl::toROSMsg	(	const pcl::PointCloud< PointT > &	cloud,
		pcl::PCLPointCloud2 &	msg
	)

Definition at line 98 of file ros/conversions.h.

template<typename CloudT >

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

Definition at line 113 of file ros/conversions.h.

void pcl::toROSMsg	(	const pcl::PCLPointCloud2 &	cloud,
		pcl::PCLImage &	msg
	)		[inline]

Definition at line 127 of file ros/conversions.h.

template<typename PointT >

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

Definition at line 393 of file common/include/pcl/common/transforms.h.

template<typename PointT >

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

Definition at line 63 of file common/include/pcl/common/transforms.h.

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
	)

Definition at line 84 of file common/include/pcl/common/transforms.h.

template<typename PointT >

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

Definition at line 109 of file common/include/pcl/common/transforms.h.

template<typename PointT >

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

Definition at line 199 of file common/include/pcl/common/transforms.h.

template<typename PointT >

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

Definition at line 224 of file common/include/pcl/common/transforms.h.

template<typename PointT >

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

Definition at line 249 of file common/include/pcl/common/transforms.h.

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]

Definition at line 352 of file common/include/pcl/common/transforms.h.

template<typename PointT , typename Scalar >

void pcl::transformPointCloudWithNormals	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Transform< Scalar, 3, Eigen::Affine > &	transform
	)

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

Parameters:

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

Note:

Can be used with cloud_in equal to cloud_out

Definition at line 143 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
	)

Definition at line 129 of file common/include/pcl/common/transforms.h.

template<typename PointT , typename Scalar >

void pcl::transformPointCloudWithNormals	(	const pcl::PointCloud< PointT > &	cloud_in,
		const std::vector< int > &	indices,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Transform< Scalar, 3, Eigen::Affine > &	transform
	)

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

Parameters:

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

Definition at line 207 of file transforms.hpp.

template<typename PointT >

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

Definition at line 149 of file common/include/pcl/common/transforms.h.

template<typename PointT , typename Scalar >

void pcl::transformPointCloudWithNormals	(	const pcl::PointCloud< PointT > &	cloud_in,
		const pcl::PointIndices &	indices,
		pcl::PointCloud< PointT > &	cloud_out,
		const Eigen::Transform< Scalar, 3, Eigen::Affine > &	transform
	)

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

Parameters:

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

Definition at line 164 of file common/include/pcl/common/transforms.h.

template<typename PointT >

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

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

template<typename PointT >

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

Definition at line 275 of file common/include/pcl/common/transforms.h.

template<typename PointT >

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

Definition at line 302 of file common/include/pcl/common/transforms.h.

template<typename PointT >

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

Definition at line 330 of file common/include/pcl/common/transforms.h.

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
	)

Definition at line 374 of file common/include/pcl/common/transforms.h.

template<typename Derived , typename OtherDerived >

Eigen::internal::umeyama_transform_matrix_type< Derived, OtherDerived >::type pcl::umeyama	(	const Eigen::MatrixBase< Derived > &	src,
		const Eigen::MatrixBase< OtherDerived > &	dst,
		bool	with_scaling = false
	)

Returns the transformation between two point sets. The algorithm is based on: "Least-squares estimation of transformation parameters between two point patterns", Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573.

It estimates parameters and such that

is minimized.

The algorithm is based on the analysis of the covariance matrix of the input point sets and where is corresponding to the dimension (which is typically small). The analysis is involving the SVD having a complexity of though the actual computational effort lies in the covariance matrix computation which has an asymptotic lower bound of when the input point sets have dimension .

Parameters:

[in]	src	Source points
[in]	dst	Destination points .
[in]	with_scaling	Sets when false is passed. (default: false)

Returns:

The homogeneous transformation

minimizing the resudiual above. This transformation is always returned as an Eigen::Matrix.

Definition at line 212 of file eigen.hpp.

template<class Type >

void pcl::write	(	std::ostream &	stream,
		Type	value
	)

Function for writing data to a stream.

Definition at line 64 of file region_xy.h.

template<class Type >

void pcl::write	(	std::ostream &	stream,
		Type *	value,
		int	nr_values
	)

Function for writing data arrays to a stream.

Definition at line 71 of file region_xy.h.

---

Variable Documentation

const unsigned int pcl::edgeTable[256]

Definition at line 59 of file marching_cubes.h.

struct pcl::ISMPeak pcl::EIGEN_ALIGN16

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

Initial value:

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

Definition at line 57 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 47 of file grid_projection.h.

const int pcl::I_SHIFT_PT[4]

Initial value:

 {
    0, 4, 5, 7
  }

Definition at line 53 of file grid_projection.h.

const int pcl::SAC_LMEDS = 1 [static]

Definition at line 47 of file method_types.h.

const int pcl::SAC_MLESAC = 5 [static]

Definition at line 51 of file method_types.h.

const int pcl::SAC_MSAC = 2 [static]

Definition at line 48 of file method_types.h.

const int pcl::SAC_PROSAC = 6 [static]

Definition at line 52 of file method_types.h.

const int pcl::SAC_RANSAC = 0 [static]

Definition at line 46 of file method_types.h.

const int pcl::SAC_RMSAC = 4 [static]

Definition at line 50 of file method_types.h.

const int pcl::SAC_RRANSAC = 3 [static]

Definition at line 49 of file method_types.h.

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

Definition at line 93 of file marching_cubes.h.