Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

pcl::_Axis | A point structure representing an Axis using its normal coordinates. (SSE friendly) |

pcl::_Normal | A point structure representing normal coordinates and the surface curvature estimate. (SSE friendly) |

pcl::tracking::_ParticleXYR | |

pcl::tracking::_ParticleXYRP | |

pcl::tracking::_ParticleXYRPY | |

pcl::tracking::_ParticleXYZR | |

pcl::tracking::_ParticleXYZRPY | |

pcl::_PointNormal | A point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate. (SSE friendly) |

pcl::_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 |

pcl::_PointWithRange | A point structure representing Euclidean xyz coordinates, padded with an extra range float |

pcl::_PointWithScale | A point structure representing a 3-D position and scale |

pcl::_PointWithViewpoint | |

pcl::_PointXYZ | |

pcl::_PointXYZHSV | |

pcl::_PointXYZI | A point structure representing Euclidean xyz coordinates, and the intensity value |

pcl::_PointXYZINormal | A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate |

pcl::_PointXYZL | |

pcl::_PointXYZRGB | |

pcl::_PointXYZRGBA | A point structure representing Euclidean xyz coordinates, and the RGBA color |

pcl::_PointXYZRGBL | |

pcl::_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: |

pcl::_ReferenceFrame | A structure representing the Local Reference Frame of a point |

pcl::AdaptiveRangeCoder | AdaptiveRangeCoder compression class |

pcl::poisson::Octree< Degree >::AdjacencyCountFunction | |

pcl::poisson::OctNode< NodeData, Real >::AdjacencyCountFunction | |

pcl::poisson::Octree< Degree >::AdjacencySetFunction | |

pcl::poisson::Allocator< T > | |

pcl::poisson::AllocatorState | |

pcl::ApproximateVoxelGrid< PointT > | ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |

pcl::tracking::ApproxNearestPairPointCloudCoherence< PointInT > | ApproxNearestPairPointCloudCoherence computes coherence between two pointclouds using the approximate nearest point pairs |

pcl::traits::asEnum< T > | |

pcl::traits::asEnum< double > | |

pcl::traits::asEnum< float > | |

pcl::traits::asEnum< int16_t > | |

pcl::traits::asEnum< int32_t > | |

pcl::traits::asEnum< int8_t > | |

pcl::traits::asEnum< uint16_t > | |

pcl::traits::asEnum< uint32_t > | |

pcl::traits::asEnum< uint8_t > | |

pcl::traits::asType< int > | |

pcl::traits::asType< sensor_msgs::PointField::FLOAT32 > | |

pcl::traits::asType< sensor_msgs::PointField::FLOAT64 > | |

pcl::traits::asType< sensor_msgs::PointField::INT16 > | |

pcl::traits::asType< sensor_msgs::PointField::INT32 > | |

pcl::traits::asType< sensor_msgs::PointField::INT8 > | |

pcl::traits::asType< sensor_msgs::PointField::UINT16 > | |

pcl::traits::asType< sensor_msgs::PointField::UINT32 > | |

pcl::traits::asType< sensor_msgs::PointField::UINT8 > | |

pcl::Axis | |

pcl::BilateralFilter< PointT > | A bilateral filter implementation for point cloud data. Uses the intensity data channel |

pcl::BilateralUpsampling< PointInT, PointOutT > | 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 |

pcl::poisson::BinaryNode< Real > | |

pcl::BivariatePolynomialT< real > | This represents a bivariate polynomial and provides some functionality for it |

pcl::BorderDescription | A structure to store if a point in a range image lies on a border between an obstacle and the background |

pcl::Boundary | A point structure representing a description of whether a point is lying on a surface boundary or not |

pcl::BoundaryEstimation< PointInT, PointNT, PointOutT > | 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 |

pcl::BoundaryEstimation< PointInT, PointNT, Eigen::MatrixXf > | BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion. The code makes use of the estimated surface normals at each point in the input dataset |

pcl::search::BruteForce< PointT > | Implementation of a simple brute force search algorithm |

pcl::octree::BufferedBranchNode< ContainerT > | |

pcl::io::ply::ply_parser::scalar_property_definition_callbacks_type::callbacks_element< T > | |

pcl::io::ply::ply_parser::list_property_definition_callbacks_type::callbacks_element< T > | |

pcl::texture_mapping::Camera | Structure to store camera pose and focal length |

pcl::visualization::Camera | Camera class holds a set of camera parameters together with the window pos/size |

pcl::ChannelProperties | ChannelProperties stores the properties of each channel in a cloud, namely: |

pcl::PCDWriter::ChannelPropertiesComparator | Internal structure used to sort the ChannelProperties in the cloud.channels map based on their offset |

pcl::Clipper3D< PointT > | Base class for 3D clipper objects |

cloud_point_index_idx | |

pcl::cloud_show< CloudT > | |

pcl::cloud_show_base | |

pcl::visualization::CloudActor | |

pcl::CloudProperties | CloudProperties stores a list of optional point cloud properties such as: |

pcl::CloudSurfaceProcessing< PointInT, PointOutT > | CloudSurfaceProcessing represents the base class for algorithms that take a point cloud as an input and produce a new output cloud that has been modified towards a better surface representation. These types of algorithms include surface smoothing, hole filling, cloud upsampling etc |

pcl::visualization::CloudViewer | Simple point cloud visualization class |

pcl::visualization::CloudViewer::CloudViewer_impl | |

pcl::octree::ColorCoding< PointT > | ColorCoding class |

pcl::Comparator< PointT > | Comparator is the base class for comparators that compare two points given some function. Currently intended for use with OrganizedConnectedComponentSegmentation |

pcl::search::Search< PointT >::Compare | |

pcl::ComparisonBase< PointT > | The (abstract) base class for the comparison object |

pcl::ComputeFailedException | |

pcl::ConditionalRemoval< PointT > | ConditionalRemoval filters data that satisfies certain conditions |

pcl::ConditionAnd< PointT > | AND condition |

pcl::ConditionBase< PointT > | Base condition class |

pcl::ConditionOr< PointT > | OR condition |

ConditionThresholdHSV< PointT > | |

pcl::octree::configurationProfile_t | |

pcl::PointCloud< Eigen::MatrixXf >::CopyFieldsChannelProperties< T > | Helper functor structure for copying channel information |

pcl::CopyIfFieldExists< PointInT, OutT > | A helper functor that can copy a specific value if the given field exists |

pcl::poisson::CoredEdgeIndex | |

pcl::poisson::CoredFileMeshData | |

pcl::poisson::CoredMeshData | |

pcl::poisson::CoredPointIndex | |

pcl::poisson::CoredVectorMeshData | |

pcl::poisson::CoredVertexIndex | |

pcl::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 |

pcl::registration::CorrespondenceEstimation< PointSource, PointTarget > | CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features |

pcl::registration::CorrespondenceEstimationNormalShooting< PointSource, PointTarget, NormalT > | CorrespondenceEstimationNormalShooting computes correspondences as points in the target cloud which have minimum distance to normals computed on the input cloud |

pcl::registration::CorrespondenceRejector | |

pcl::registration::CorrespondenceRejectorDistance | |

pcl::registration::CorrespondenceRejectorFeatures | |

pcl::registration::CorrespondenceRejectorMedianDistance | |

pcl::registration::CorrespondenceRejectorOneToOne | CorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences. Correspondences with the same match index are removed and only the one with smallest distance between query and match are kept. That is, considering match->query 1-m correspondences are removed leaving only 1-1 correspondences |

pcl::registration::CorrespondenceRejectorSampleConsensus< PointT > | CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers) |

pcl::registration::CorrespondenceRejectorSurfaceNormal | |

pcl::registration::CorrespondenceRejectorTrimmed | CorrespondenceRejectorTrimmed implements a correspondence rejection for ICP-like registration algorithms that uses only the best 'k' correspondences where 'k' is some estimate of the overlap between the two point clouds being registered |

pcl::registration::CorrespondenceRejectorVarTrimmed | |

pcl::CropBox< PointT > | CropBox is a filter that allows the user to filter all the data inside of a given box |

pcl::CropBox< sensor_msgs::PointCloud2 > | CropBox is a filter that allows the user to filter all the data inside of a given box |

pcl::CropHull< PointT > | Filter points that lie inside or outside a 3D closed surface or 2D closed polygon, as generated by the ConvexHull or ConcaveHull classes |

pcl::poisson::Cube | |

pcl::CustomPointRepresentation< PointDefault > | CustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point |

pcl::CVFHEstimation< PointInT, PointNT, PointOutT > | CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in: |

pcl::registration::DataContainer< PointT, NormalT > | |

pcl::registration::DataContainerInterface | |

pcl::traits::datatype< PointT, Tag > | |

pcl::traits::decomposeArray< T > | |

pcl::DefaultFeatureRepresentation< PointDefault > | 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) |

pcl::DefaultPointRepresentation< PointDefault > | DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types |

pcl::DefaultPointRepresentation< FPFHSignature33 > | |

pcl::DefaultPointRepresentation< NormalBasedSignature12 > | |

pcl::DefaultPointRepresentation< PFHRGBSignature250 > | |

pcl::DefaultPointRepresentation< PFHSignature125 > | |

pcl::DefaultPointRepresentation< PointNormal > | |

pcl::DefaultPointRepresentation< PointXYZ > | |

pcl::DefaultPointRepresentation< PointXYZI > | |

pcl::DefaultPointRepresentation< PPFSignature > | |

pcl::DefaultPointRepresentation< ShapeContext > | |

pcl::DefaultPointRepresentation< SHOT1344 > | |

pcl::DefaultPointRepresentation< SHOT352 > | |

pcl::DefaultPointRepresentation< VFHSignature308 > | |

pcl::tracking::DistanceCoherence< PointInT > | DistanceCoherence computes coherence between two points from the distance between them. the coherence is calculated by 1 / (1 + weight * d^2 ) |

pcl::poisson::Octree< Degree >::DivergenceFunction | |

pcl::apps::DominantPlaneSegmentation< PointType > | DominantPlaneSegmentation performs euclidean segmentation on a scene assuming that a dominant plane exists |

pcl::GreedyProjectionTriangulation< PointInT >::doubleEdge | Struct for storing the edges starting from a fringe point |

Dummy | |

pcl::EarClipping | The ear clipping triangulation algorithm. The code is inspired by Flavien Brebion implementation, which is in n^3 and does not handle holes |

pcl::poisson::Edge | |

pcl::EdgeAwarePlaneComparator< PointT, PointNT > | 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 |

pcl::poisson::EdgeIndex | |

pcl::registration::ELCH< PointT > | ELCH (Explicit Loop Closing Heuristic) class |

pcl::io::ply::ply_parser::element | |

pcl::search::BruteForce< PointT >::Entry | |

pcl::search::OrganizedNeighbor< PointT >::Entry | |

pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ErrorFunctor | |

pcl::ESFEstimation< PointInT, PointOutT > | 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 |

pcl::ESFSignature640 | A point structure representing the Ensemble of Shape Functions (ESF) |

pcl::EuclideanClusterComparator< PointT, PointNT, PointLT > | 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 |

pcl::EuclideanClusterExtraction< PointT > | EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense |

pcl::EuclideanPlaneCoefficientComparator< PointT, PointNT > | 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 |

EventHelper | |

pcl::visualization::PCLHistogramVisualizer::ExitCallback | |

pcl::visualization::ImageViewer::ExitCallback | |

pcl::visualization::PCLVisualizer::ExitCallback | |

pcl::visualization::Window::ExitCallback | |

pcl::visualization::PCLHistogramVisualizer::ExitMainLoopTimerCallback | |

pcl::visualization::ImageViewer::ExitMainLoopTimerCallback | |

pcl::visualization::PCLVisualizer::ExitMainLoopTimerCallback | |

pcl::visualization::Window::ExitMainLoopTimerCallback | |

pcl::ExtractIndices< PointT > | ExtractIndices extracts a set of indices from a point cloud |

pcl::ExtractIndices< sensor_msgs::PointCloud2 > | ExtractIndices extracts a set of indices from a point cloud. Usage examples: |

pcl::ExtractPolygonalPrismData< PointT > | 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 |

pcl::poisson::Octree< Degree >::FaceEdgesFunction | |

pcl::Feature< PointInT, PointOutT > | Feature represents the base feature class. Some generic 3D operations that are applicable to all features are defined here as static methods |

FeatureCloud | |

pcl::registration::CorrespondenceRejectorFeatures::FeatureContainer< FeatureT > | An inner class containing pointers to the source and target feature clouds and the parameters needed to perform the correspondence search. This class extends FeatureContainerInterface, which contains abstract methods for any methods that do not depend on the FeatureT --- these methods can thus be called from a pointer to FeatureContainerInterface without casting to the derived class |

pcl::registration::CorrespondenceRejectorFeatures::FeatureContainerInterface | |

pcl::FeatureFromLabels< PointInT, PointLT, PointOutT > | |

pcl::FeatureFromNormals< PointInT, PointNT, PointOutT > | |

pcl::Narf::FeaturePointRepresentation | |

pcl::FeatureWithLocalReferenceFrames< PointInT, PointRFT > | FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which need a local reference frame at each input keypoint |

pcl::detail::FieldAdder< PointT > | |

pcl::FieldComparison< PointT > | The field-based specialization of the comparison object |

pcl::traits::fieldList< PointT > | |

pcl::detail::FieldMapper< PointT > | |

pcl::detail::FieldMapping | |

pcl::FieldMatches< PointT, Tag > | |

pcl::octree::OctreeBreadthFirstIterator< DataT, OctreeT >::FIFOElement | |

pcl::FileReader | Point Cloud Data (FILE) file format reader interface. Any (FILE) format file reader should implement its virtual methodes |

pcl::FileWriter | Point Cloud Data (FILE) file format writer. Any (FILE) format file reader should implement its virtual methodes |

pcl::Filter< PointT > | Filter represents the base filter class. All filters must inherit from this interface |

pcl::Filter< sensor_msgs::PointCloud2 > | Filter represents the base filter class. All filters must inherit from this interface |

pcl::FilterIndices< PointT > | 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) |

pcl::FilterIndices< sensor_msgs::PointCloud2 > | FilterIndices represents the base class for filters that are about binary point removal. All derived classes have to implement the filter (PointCloud &output) and the filter (std::vector<int> &indices) methods. Ideally they also make use of the negative_, keep_organized_ and extract_removed_indices_ systems. The distinguishment between the negative_ and extract_removed_indices_ systems only makes sense if the class automatically filters non-finite entries in the filtering methods (recommended) |

pcl::search::FlannSearch< PointT, FlannDistance >::FlannIndexCreator | Helper class that creates a FLANN index from a given FLANN matrix. To use a FLANN index type with FlannSearch, implement this interface and pass an object of the new type to the FlannSearch constructor. See the implementation of KdTreeIndexCreator for an example |

pcl::search::FlannSearch< PointT, FlannDistance > | search::FlannSearch is a generic FLANN wrapper class for the new search interface. It is able to wrap any FLANN index type, e.g. the kd tree as well as indices for high-dimensional searches and intended as a more powerful and cleaner successor to KdTreeFlann |

pcl::visualization::FloatImageUtils | |

pcl::for_each_type_impl< done > | |

pcl::for_each_type_impl< false > | |

pcl::FPFHEstimation< PointInT, PointNT, PointOutT > | FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals |

pcl::FPFHEstimation< PointInT, PointNT, Eigen::MatrixXf > | FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals |

pcl::FPFHEstimationOMP< PointInT, PointNT, PointOutT > | 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 |

pcl::FPFHSignature33 | A point structure representing the Fast Point Feature Histogram (FPFH) |

pcl::visualization::FPSCallback | |

pcl::poisson::FunctionData< Degree, Real > | |

pcl::registration::TransformationEstimationLM< PointSource, PointTarget >::Functor< _Scalar, NX, NY > | |

pcl::Functor< _Scalar, NX, NY > | |

pcl::GaussianKernel | |

pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget > | 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 |

pcl::GFPFHSignature16 | A point structure representing the GFPFH descriptor with 16 bins |

pcl::Grabber | Grabber interface for PCL 1.x device drivers |

pcl::GreedyProjectionTriangulation< PointInT > | 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 |

pcl::GridProjection< PointNT > | Grid projection surface reconstruction method |

pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT > | HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals |

__gnu_cxx::hash< const long long > | |

__gnu_cxx::hash< const unsigned long long > | |

__gnu_cxx::hash< long long > | |

__gnu_cxx::hash< unsigned long long > | |

pcl::PPFHashMapSearch::HashKeyStruct | Data structure to hold the information for the key in the feature hash map of the PPFHashMapSearch class |

pcl::ApproximateVoxelGrid< PointT >::he | |

pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT, NrDims > | |

pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT[NrDims], NrDims > | |

pcl::Histogram< N > | A point structure representing an N-D histogram |

pcl::tracking::HSVColorCoherence< PointInT > | HSVColorCoherence computes coherence between the two points from the color difference between them. the color difference is calculated in HSV color space. the coherence is calculated by 1 / ( 1 + w * (w_h^2 * h_diff^2 + w_s^2 * s_diff^2 + w_v^2 * v_diff^2)) |

pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::HuberPenalty | |

ICCVTutorial< FeatureType > | |

pcl::visualization::ImageViewer | ImageViewer is a class for 2D image visualization |

pcl::DefaultFeatureRepresentation< PointDefault >::IncrementFunctor | |

pcl::InitFailedException | An exception thrown when init can not be performed should be used in all the PCLBase class inheritants |

pcl::IntegralImage2D< DataType, Dimension > | Determines an integral image representation for a given organized data array |

pcl::IntegralImage2D< DataType, 1 > | Partial template specialization for integral images with just one channel |

pcl::IntegralImageNormalEstimation< PointInT, PointOutT > | Surface normal estimation on organized data using integral images |

pcl::IntegralImageTypeTraits< DataType > | |

pcl::IntegralImageTypeTraits< char > | |

pcl::IntegralImageTypeTraits< float > | |

pcl::IntegralImageTypeTraits< int > | |

pcl::IntegralImageTypeTraits< short > | |

pcl::IntegralImageTypeTraits< unsigned char > | |

pcl::IntegralImageTypeTraits< unsigned int > | |

pcl::IntegralImageTypeTraits< unsigned short > | |

pcl::common::IntensityFieldAccessor< PointT > | |

pcl::common::IntensityFieldAccessor< pcl::PointNormal > | |

pcl::common::IntensityFieldAccessor< pcl::PointXYZRGB > | |

pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBA > | |

pcl::IntensityGradient | A point structure representing the intensity gradient of an XYZI point cloud |

pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT > | 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 |

pcl::IntensityGradientEstimation< PointInT, PointNT, Eigen::MatrixXf > | IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values. The intensity gradient at a given point will be a vector orthogonal to the surface normal and pointing in the direction of the greatest increase in local intensity; the vector's magnitude indicates the rate of intensity change |

pcl::IntensitySpinEstimation< PointInT, PointOutT > | 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: |

pcl::IntensitySpinEstimation< PointInT, Eigen::MatrixXf > | IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity. For more information about the intensity-domain spin image descriptor, see: |

pcl::InterestPoint | A point structure representing an interest point with Euclidean xyz coordinates, and an interest value |

pcl::intersect< Sequence1, Sequence2 > | |

pcl::InvalidConversionException | An exception that is thrown when a PointCloud2 message cannot be converted into a PCL type |

pcl::InvalidSACModelTypeException | An exception that is thrown when a sample consensus model doesn't have the correct number of samples defined in model_types.h |

pcl::IOException | An exception that is thrown during an IO error (typical read/write errors) |

openni_wrapper::IRImage | Class containing just a reference to IR meta data |

pcl::PosesFromMatches::PoseEstimate::IsBetter | |

pcl::IsNotDenseException | An exception that is thrown when a PointCloud is not dense but is attemped to be used as dense |

pcl::IterativeClosestPoint< PointSource, PointTarget > | IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm. The transformation is estimated based on Singular Value Decomposition (SVD) |

pcl::IterativeClosestPointNonLinear< PointSource, PointTarget > | IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend. The resultant transformation is optimized as a quaternion |

pcl::KdTree< PointT > | KdTree represents the base spatial locator class for kd-tree implementations |

pcl::search::KdTree< PointT > | search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure. KdTree 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 |

pcl::KdTreeFLANN< PointT, Dist > | 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 |

pcl::KdTreeFLANN< Eigen::MatrixXf > | KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. The class is making use of the FLANN (Fast Library for Approximate Nearest Neighbor) project by Marius Muja and David Lowe |

pcl::search::FlannSearch< PointT, FlannDistance >::KdTreeIndexCreator | Creates a FLANN KdTreeSingleIndex from the given input data |

pcl::KernelWidthTooSmallException | An exception that is thrown when the kernel size is too small |

pcl::visualization::KeyboardEvent | |

pcl::Keypoint< PointInT, PointOutT > | Keypoint represents the base class for key points |

KeypointT | |

pcl::tracking::KLDAdaptiveParticleFilterOMPTracker< PointInT, StateT > | KLDAdaptiveParticleFilterOMPTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method. The number of the particles changes adaptively based on KLD sampling [D. Fox, NIPS-01], [D.Fox, IJRR03]. and the computation of the weights of the particles is parallelized using OpenMP |

pcl::tracking::KLDAdaptiveParticleFilterTracker< PointInT, StateT > | KLDAdaptiveParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method. The number of the particles changes adaptively based on KLD sampling [D. Fox, NIPS-01], [D.Fox, IJRR03] |

pcl::Label | |

pcl::LabeledEuclideanClusterExtraction< PointT > | LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info |

pcl::poisson::Octree< Degree >::LaplacianMatrixFunction | |

pcl::poisson::Octree< Degree >::LaplacianProjectionFunction | |

pcl::visualization::ImageViewer::Layer | Internal structure describing a layer |

pcl::visualization::ImageViewer::LayerComparator | |

pcl::UniformSampling< PointInT >::Leaf | Simple structure to hold an nD centroid and the number of points in a leaf |

pcl::GridProjection< PointNT >::Leaf | Data leaf |

pcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGrid::Leaf | |

pcl::LeastMedianSquares< PointT > | 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 |

pcl::LineIterator | Organized Index Iterator for iterating over the "pixels" for a given line using the Bresenham algorithm. Supports 4 and 8 neighborhood connectivity |

pcl::io::ply::ply_parser::list_property< SizeType, ScalarType > | |

pcl::io::ply::ply_parser::list_property_begin_callback_type< SizeType, ScalarType > | |

pcl::io::ply::ply_parser::list_property_definition_callback_type< SizeType, ScalarType > | |

pcl::io::ply::ply_parser::list_property_definition_callbacks_type | |

pcl::io::ply::ply_parser::list_property_element_callback_type< SizeType, ScalarType > | |

pcl::io::ply::ply_parser::list_property_end_callback_type< SizeType, ScalarType > | |

pcl::RangeImageBorderExtractor::LocalSurface | Stores some information extracted from the neighborhood of a point |

pcl::MarchingCubes< PointNT > | 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: |

pcl::poisson::MarchingCubes | |

pcl::MarchingCubesHoppe< PointNT > | 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 |

pcl::MarchingCubesRBF< PointNT > | 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 |

pcl::poisson::MarchingSquares | |

pcl::poisson::MatrixEntry< T > | |

pcl::MaximumLikelihoodSampleConsensus< PointT > | 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 |

Mesh | |

pcl::MeshConstruction< PointInT > | 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 |

pcl::MeshProcessing | MeshProcessing represents the base class for mesh processing algorithms |

pcl::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 |

pcl::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 |

pcl::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 |

pcl::MEstimatorSampleConsensus< PointT > | 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 |

pcl::MovingLeastSquares< PointInT, PointOutT >::MLSResult | Data structure used to store the results of the MLS fitting |

pcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGrid | A minimalistic implementation of a voxel grid, necessary for the point cloud upsampling |

pcl::MomentInvariants | A point structure representing the three moment invariants |

pcl::MomentInvariantsEstimation< PointInT, PointOutT > | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |

pcl::MomentInvariantsEstimation< PointInT, Eigen::MatrixXf > | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |

pcl::visualization::MouseEvent | |

pcl::MovingLeastSquares< PointInT, PointOutT > | 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 |

pcl::MovingLeastSquaresOMP< PointInT, PointOutT > | MovingLeastSquaresOMP represent an OpenMP implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation |

pcl::MultiscaleFeaturePersistence< PointSource, PointFeature > | 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 |

MyPoint | |

MyPointRepresentationXY | |

pcl::traits::name< PointT, Tag, dummy > | |

pcl::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 |

pcl::Narf36 | A point structure representing the Narf descriptor |

pcl::NarfDescriptor | |

pcl::NarfKeypoint | NARF (Normal Aligned Radial Feature) keypoints. Input is a range image, output the indices of the keypoints |

pcl::NdCentroidFunctor< PointT > | Helper functor structure for n-D centroid estimation |

pcl::NdConcatenateFunctor< PointInT, PointOutT > | Helper functor structure for concatenate |

pcl::NdCopyEigenPointFunctor< PointOutT > | Helper functor structure for copying data between an Eigen type and a PointT |

pcl::PointCloud< Eigen::MatrixXf >::NdCopyEigenPointFunctor< PointOutT, PointInT > | Helper functor structure for copying data between an Eigen type and a PointT |

pcl::NdCopyPointEigenFunctor< PointInT > | Helper functor structure for copying data between an Eigen type and a PointT |

pcl::PointCloud< Eigen::MatrixXf >::NdCopyPointEigenFunctor< PointInT, PointOutT > | Helper functor structure for copying data between an Eigen type and a PointT |

pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor | |

pcl::tracking::NearestPairPointCloudCoherence< PointInT > | NearestPairPointCloudCoherence computes coherence between two pointclouds using the nearest point pairs |

pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT >::Neighbor | |

pcl::poisson::OctNode< NodeData, Real >::NeighborKey | |

pcl::poisson::OctNode< NodeData, Real >::NeighborKey2 | |

pcl::poisson::OctNode< NodeData, Real >::Neighbors | |

pcl::poisson::OctNode< NodeData, Real >::Neighbors2 | |

NILinemod | |

pcl::poisson::NMatrixEntry< T, Dim > | |

pcl::GreedyProjectionTriangulation< PointInT >::nnAngle | Struct for storing the angles to nearest neighbors |

pcl::NNClassification< PointT > | 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 |

pcl::Normal | |

pcl::NormalBasedSignature12 | A point structure representing the Normal Based Signature for a feature matrix of 4-by-3 |

pcl::NormalBasedSignatureEstimation< PointT, PointNT, PointFeature > | 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 |

pcl::tracking::NormalCoherence< PointInT > | NormalCoherence computes coherence between two points from the angle between their normals. the coherence is calculated by 1 / (1 + weight * theta^2 ) |

pcl::NormalEstimation< PointInT, PointOutT > | 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 |

pcl::NormalEstimation< PointInT, Eigen::MatrixXf > | NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures |

pcl::NormalEstimationOMP< PointInT, PointOutT > | NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard |

pcl::NormalEstimationOMP< PointInT, Eigen::MatrixXf > | NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard |

pcl::NormalSpaceSampling< PointT, NormalT > | NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every point |

pcl::NotEnoughPointsException | An exception that is thrown when the number of correspondants is not equal to the minimum required |

pcl::poisson::NVector< T, Dim > | |

ObjectFeatures | |

ObjectModel | |

ObjectRecognition | |

ObjectRecognitionParameters | |

pcl::poisson::OctNode< NodeData, Real > | |

pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT > | search::Octree is a wrapper class which implements nearest neighbor search operations based on the pcl::octree::Octree structure |

pcl::poisson::Octree< Degree > | |

pcl::octree::Octree2BufBase< DataT, LeafT, BranchT > | Octree double buffer class |

pcl::octree::OctreeBase< DataT, LeafT, BranchT > | Octree class |

pcl::octree::OctreeBranchNode< ContainerT > | Abstract octree branch class |

pcl::octree::OctreeBreadthFirstIterator< DataT, OctreeT > | Octree iterator class |

pcl::octree::OctreeContainerDataT< DataT > | Octree leaf class that does store a single DataT element |

pcl::octree::OctreeContainerDataTVector< DataT > | Octree leaf class that does store a vector of DataT elements |

pcl::octree::OctreeContainerEmpty< DataT > | Octree leaf class that does not store any information |

pcl::octree::OctreeDepthFirstIterator< DataT, OctreeT > | Octree iterator class |

pcl::octree::OctreeIteratorBase< DataT, OctreeT > | Abstract octree iterator class |

pcl::octree::OctreeKey | Octree key class |

pcl::octree::OctreeLeafNode< ContainerT > | Abstract octree leaf class |

pcl::octree::OctreeLeafNodeIterator< DataT, OctreeT > | Octree leaf node iterator class |

pcl::octree::OctreeNode | Abstract octree node class |

pcl::octree::OctreeNodePool< NodeT > | Octree node pool |

pcl::octree::OctreePointCloud< PointT, LeafT, BranchT, OctreeT > | Octree pointcloud class |

pcl::octree::OctreePointCloudChangeDetector< PointT, LeafT, BranchT > | Octree pointcloud change detector class |

pcl::octree::OctreePointCloudDensity< PointT, LeafT, BranchT > | Octree pointcloud density class |

pcl::octree::OctreePointCloudDensityContainer< DataT > | Octree pointcloud density leaf node class |

pcl::octree::OctreePointCloudOccupancy< PointT, LeafT, BranchT > | Octree pointcloud occupancy class |

pcl::octree::OctreePointCloudPointVector< PointT, LeafT, BranchT, OctreeT > | Octree pointcloud point vector class |

pcl::octree::OctreePointCloudSearch< PointT, LeafT, BranchT > | Octree pointcloud search class |

pcl::octree::OctreePointCloudSinglePoint< PointT, LeafT, BranchT, OctreeT > | Octree pointcloud single point class |

pcl::octree::OctreePointCloudVoxelCentroid< PointT, LeafT, BranchT > | Octree pointcloud voxel centroid class |

OctreeViewer | |

pcl::traits::offset< PointT, Tag > | |

OpenNI3DConcaveHull< PointType > | |

OpenNI3DConvexHull< PointType > | |

OpenNICapture | |

OpenNIChangeViewer | |

OpenNIFastMesh< PointType > | |

OpenNIFeaturePersistence< PointType > | |

OpenNIGrabFrame< PointType > | |

OpenNIIntegralImageNormalEstimation< PointType > | |

OpenNIIO< PointType > | |

OpenNIOrganizedMultiPlaneSegmentation | |

OpenNIPassthrough< PointType > | |

OpenNIPlanarSegmentation< PointType > | |

OpenNISegmentTracking< PointType > | |

OpenNISmoothing< PointType > | |

OpenNIUniformSampling | |

OpenNIVoxelGrid< PointType > | |

pcl::registration::TransformationEstimationLM< PointSource, PointTarget >::OptimizationFunctor | |

pcl::SampleConsensusModelCircle2D< PointT >::OptimizationFunctor | Functor for the optimization function |

pcl::SampleConsensusModelCone< PointT, PointNT >::OptimizationFunctor | Functor for the optimization function |

pcl::SampleConsensusModelCylinder< PointT, PointNT >::OptimizationFunctor | Functor for the optimization function |

pcl::SampleConsensusModelSphere< PointT >::OptimizationFunctor | |

pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices | Optimization functor structure |

pcl::registration::TransformationEstimationLM< PointSource, PointTarget >::OptimizationFunctorWithIndices | |

pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT > | 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 |

pcl::OrganizedFastMesh< PointInT > | Simple triangulation/surface reconstruction for organized point clouds. Neighboring points (pixels in image space) are connected to construct a triangular mesh |

pcl::OrganizedIndexIterator | Base class for iterators on 2-dimensional maps like images/organized clouds etc |

pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT > | 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 |

pcl::search::OrganizedNeighbor< PointT > | OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds |

OrganizedSegmentationDemo | |

pcl::PackedHSIComparison< PointT > | A packed HSI specialization of the comparison object |

pcl::PackedRGBComparison< PointT > | A packed rgb specialization of the comparison object |

pcl::io::ply::ply_parser::list_property_definition_callbacks_type::pair_with< T > | |

pcl::RangeImageBorderExtractor::Parameters | Parameters used in this class |

pcl::NarfDescriptor::Parameters | |

pcl::PolynomialCalculationsT< real >::Parameters | Parameters used in this class |

pcl::PosesFromMatches::Parameters | Parameters used in this class |

pcl::NarfKeypoint::Parameters | Parameters used in this class |

pcl::tracking::ParticleFilterOMPTracker< PointInT, StateT > | ParticleFilterOMPTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method in parallel, using the OpenMP standard |

pcl::tracking::ParticleFilterTracker< PointInT, StateT > | ParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method |

pcl::tracking::ParticleXYR | |

pcl::tracking::ParticleXYRP | |

pcl::tracking::ParticleXYRPY | |

pcl::tracking::ParticleXYZR | |

pcl::tracking::ParticleXYZRPY | |

pcl::PassThrough< PointT > | PassThrough passes points in a cloud based on constraints for one particular field of the point type |

pcl::PassThrough< sensor_msgs::PointCloud2 > | PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints |

pcl::PCA< PointT > | |

pcl::PCDGrabber< PointT > | |

pcl::PCDGrabberBase | Base class for PCD file grabber |

pcl::PCDGrabberBase::PCDGrabberImpl | |

PCDOrganizedMultiPlaneSegmentation< PointT > | |

pcl::PCDReader | Point Cloud Data (PCD) file format reader |

pcl::PCDWriter | Point Cloud Data (PCD) file format writer |

pcl::PCLBase< PointT > | PCL base class. Implements methods that are used by all PCL objects |

pcl::PCLBase< sensor_msgs::PointCloud2 > | |

pcl::PCLException | A base class for all pcl exceptions which inherits from std::runtime_error |

pcl::visualization::PCLHistogramVisualizer | PCL histogram visualizer main class |

pcl::visualization::PCLHistogramVisualizerInteractorStyle | PCL histogram visualizer interactory style class |

pcl::visualization::PCLImageCanvasSource2D | PclImageCanvasSource2D represents our own custom version of vtkImageCanvasSource2D, used by the ImageViewer class |

pcl::PCLIOException | Base exception class for I/O operations |

PCLMobileServer< PointType > | |

pcl::PCLSurfaceBase< PointInT > | 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: |

pcl::visualization::PCLVisualizer | PCL Visualizer main class |

pcl::visualization::PCLVisualizerInteractor | The PCLVisualizer interactor |

pcl::visualization::PCLVisualizerInteractorStyle | PCLVisualizerInteractorStyle defines an unique, custom VTK based interactory style for PCL Visualizer applications. Besides defining the rendering style, we also create a list of custom actions that are triggered on different keys being pressed: |

pcl::PFHEstimation< PointInT, PointNT, PointOutT > | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |

pcl::PFHEstimation< PointInT, PointNT, Eigen::MatrixXf > | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |

pcl::PFHRGBEstimation< PointInT, PointNT, PointOutT > | |

pcl::PFHRGBSignature250 | A point structure representing the Point Feature Histogram with colors (PFHRGB) |

pcl::PFHSignature125 | A point structure representing the Point Feature Histogram (PFH) |

pcl::PiecewiseLinearFunction | This provides functionalities to efficiently return values for piecewise linear function |

pcl::PlanarPolygon< PointT > | PlanarPolygon represents a planar (2D) polygon, potentially in a 3D space |

pcl::PlanarPolygonFusion< PointT > | 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 |

pcl::PlanarRegion< PointT > | 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 |

pcl::PlaneClipper3D< PointT > | Implementation of a plane clipper in 3D |

pcl::PlaneCoefficientComparator< PointT, PointNT > | 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 |

pcl::PlaneRefinementComparator< PointT, PointNT, PointLT > | 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 |

PlaneSegTest | |

Player | |

pcl::io::ply::ply_parser | |

ply_to_obj_converter | |

ply_to_ply_converter | |

ply_to_raw_converter | |

pcl::PLYReader | Point Cloud Data (PLY) file format reader |

pcl::PLYWriter | Point Cloud Data (PLY) file format writer |

pcl::traits::POD< PointT > | |

pcl::poisson::Point3D< Real > | |

pcl::PointCloud< PointT > | PointCloud represents the base class in PCL for storing collections of 3D points |

pcl::PointCloud< Eigen::MatrixXf > | PointCloud specialization for Eigen matrices. For advanced users only! |

PointCloudBuffers | |

pcl::tracking::PointCloudCoherence< PointInT > | PointCloudCoherence is a base class to compute coherence between the two PointClouds |

pcl::visualization::PointCloudColorHandler< PointT > | Base Handler class for PointCloud colors |

pcl::visualization::PointCloudColorHandler< sensor_msgs::PointCloud2 > | Base Handler class for PointCloud colors |

pcl::visualization::PointCloudColorHandlerCustom< PointT > | Handler for predefined user colors. The color at each point will be drawn as the use given R, G, B values |

pcl::visualization::PointCloudColorHandlerCustom< sensor_msgs::PointCloud2 > | Handler for predefined user colors. The color at each point will be drawn as the use given R, G, B values |

pcl::visualization::PointCloudColorHandlerGenericField< PointT > | Generic field handler class for colors. Uses an user given field to extract 1D data and display the color at each point using a min-max lookup table |

pcl::visualization::PointCloudColorHandlerGenericField< sensor_msgs::PointCloud2 > | Generic field handler class for colors. Uses an user given field to extract 1D data and display the color at each point using a min-max lookup table |

pcl::visualization::PointCloudColorHandlerHSVField< PointT > | HSV handler class for colors. Uses the data present in the "h", "s", "v" fields as the color at each point |

pcl::visualization::PointCloudColorHandlerHSVField< sensor_msgs::PointCloud2 > | HSV handler class for colors. Uses the data present in the "h", "s", "v" fields as the color at each point |

pcl::visualization::PointCloudColorHandlerRandom< PointT > | Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen) |

pcl::visualization::PointCloudColorHandlerRandom< sensor_msgs::PointCloud2 > | Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen) |

pcl::visualization::PointCloudColorHandlerRGBField< PointT > | RGB handler class for colors. Uses the data present in the "rgb" or "rgba" fields as the color at each point |

pcl::visualization::PointCloudColorHandlerRGBField< sensor_msgs::PointCloud2 > | RGB handler class for colors. Uses the data present in the "rgb" or "rgba" fields as the color at each point |

pcl::octree::PointCloudCompression< PointT, LeafT, BranchT, OctreeT > | Octree pointcloud compression class |

pcl::visualization::PointCloudGeometryHandler< PointT > | Base handler class for PointCloud geometry |

pcl::visualization::PointCloudGeometryHandler< sensor_msgs::PointCloud2 > | Base handler class for PointCloud geometry |

pcl::visualization::PointCloudGeometryHandlerCustom< PointT > | Custom handler class for PointCloud geometry. Given an input dataset and three user defined fields, all data present in them is extracted and displayed on screen as XYZ data |

pcl::visualization::PointCloudGeometryHandlerCustom< sensor_msgs::PointCloud2 > | Custom handler class for PointCloud geometry. Given an input dataset and three user defined fields, all data present in them is extracted and displayed on screen as XYZ data |

pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< PointT > | Surface normal handler class for PointCloud geometry. Given an input dataset, all data present in fields "normal_x", "normal_y", and "normal_z" is extracted and dislayed on screen as XYZ data |

pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< sensor_msgs::PointCloud2 > | Surface normal handler class for PointCloud geometry. Given an input dataset, all data present in fields "normal_x", "normal_y", and "normal_z" is extracted and dislayed on screen as XYZ data |

pcl::visualization::PointCloudGeometryHandlerXYZ< PointT > | XYZ handler class for PointCloud geometry. Given an input dataset, all XYZ data present in fields "x", "y", and "z" is extracted and displayed on screen |

pcl::visualization::PointCloudGeometryHandlerXYZ< sensor_msgs::PointCloud2 > | XYZ handler class for PointCloud geometry. Given an input dataset, all XYZ data present in fields "x", "y", and "z" is extracted and displayed on screen |

pcl::octree::PointCoding< PointT > | PointCoding class |

pcl::tracking::PointCoherence< PointInT > | PointCoherence is a base class to compute coherence between the two points |

pcl::PointCorrespondence3D | Representation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g. from feature matching) |

pcl::PointCorrespondence6D | Representation of a (possible) correspondence between two points (e.g. from feature matching), that encode complete 6DOF transoformations |

pcl::PointDataAtOffset< PointT > | A datatype that enables type-correct comparisons |

pcl::poisson::Octree< Degree >::PointIndexValueAndNormalFunction | |

pcl::poisson::Octree< Degree >::PointIndexValueFunction | |

pcl::PointNormal | |

pcl::visualization::PointPickingCallback | |

pcl::visualization::PointPickingEvent | |

pcl::PointRepresentation< PointT > | PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector |

pcl::PointSurfel | |

pcl::PointWithRange | |

pcl::PointWithScale | |

pcl::PointWithViewpoint | A point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen |

pcl::PointXY | A 2D point structure representing Euclidean xy coordinates |

pcl::PointXYZ | A point structure representing Euclidean xyz coordinates. (SSE friendly) |

PointXYZFPFH33 | |

pcl::PointXYZHSV | |

pcl::PointXYZI | |

pcl::PointXYZINormal | |

pcl::PointXYZL | |

pcl::PointXYZRGB | A point structure representing Euclidean xyz coordinates, and the RGB color |

pcl::PointXYZRGBA | |

pcl::PointXYZRGBL | |

pcl::PointXYZRGBNormal | |

pcl::Poisson< PointNT > | The Poisson surface reconstruction algorithm |

pcl::poisson::Polynomial< Degree > | |

pcl::PolynomialCalculationsT< real > | This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials |

pcl::PosesFromMatches::PoseEstimate | A result of the pose estimation process |

pcl::PosesFromMatches | Calculate 3D transformation based on point correspondencdes |

pcl::PPFRegistration< PointSource, PointTarget >::PoseWithVotes | Structure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes |

pcl::PPFEstimation< PointInT, PointNT, PointOutT > | 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 |

pcl::PPFEstimation< PointInT, PointNT, Eigen::MatrixXf > | Class that calculates the "surflet" features for each pair in the given pointcloud. Please refer to the following publication for more details: B. Drost, M. Ulrich, N. Navab, S. Ilic Model Globally, Match Locally: Efficient and Robust 3D Object Recognition 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 13-18 June 2010, San Francisco, CA |

pcl::PPFHashMapSearch | |

pcl::PPFRegistration< PointSource, PointTarget > | 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 |

pcl::PPFRGBEstimation< PointInT, PointNT, PointOutT > | |

pcl::PPFRGBRegionEstimation< PointInT, PointNT, PointOutT > | |

pcl::PPFRGBSignature | A point structure for storing the Point Pair Color Feature (PPFRGB) values |

pcl::PPFSignature | A point structure for storing the Point Pair Feature (PPF) values |

pcl::poisson::PPolynomial< Degree > | |

pcl::PrincipalCurvatures | A point structure representing the principal curvatures and their magnitudes |

pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT > | PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals |

pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, Eigen::MatrixXf > | PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals |

pcl::PrincipalRadiiRSD | A point structure representing the minimum and maximum surface radii (in meters) computed using RSD |

pcl::octree::OctreePointCloudSearch< PointT, LeafT, BranchT >::prioBranchQueueEntry | Priority queue entry for branch nodes |

pcl::octree::OctreePointCloudSearch< PointT, LeafT, BranchT >::prioPointQueueEntry | Priority queue entry for point candidates |

prioPointQueueEntry | |

pcl::ProgressiveSampleConsensus< PointT > | 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 |

pcl::ProjectInliers< PointT > | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |

pcl::ProjectInliers< sensor_msgs::PointCloud2 > | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |

pcl::io::ply::ply_parser::property | |

pcl::PyramidFeatureHistogram< PointFeature > | 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 |

pcl::PyramidFeatureHistogram< PointFeature >::PyramidFeatureHistogramLevel | Structure for representing a single pyramid histogram level |

pcl::RadiusOutlierRemoval< PointT > | RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have |

pcl::RadiusOutlierRemoval< sensor_msgs::PointCloud2 > | RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K |

pcl::RandomizedMEstimatorSampleConsensus< PointT > | 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) |

pcl::RandomizedRandomSampleConsensus< PointT > | 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 |

pcl::RandomSample< PointT > | 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 |

pcl::RandomSample< sensor_msgs::PointCloud2 > | RandomSample applies a random sampling with uniform probability |

pcl::RandomSampleConsensus< PointT > | 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 |

pcl::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 |

pcl::RangeImageBorderExtractor | Extract obstacle borders from range images, meaning positions where there is a transition from foreground to background |

pcl::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 |

pcl::visualization::RangeImageVisualizer | Range image visualizer class |

Recorder | |

pcl::ReferenceFrame | |

pcl::poisson::Octree< Degree >::RefineFunction | |

pcl::Region3D< PointT > | Region3D represents summary statistics of a 3D collection of points |

pcl::Registration< PointSource, PointTarget > | Registration represents the base registration class. All 3D registration methods should inherit from this class |

pcl::RegistrationVisualizer< PointSource, PointTarget > | 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 |

RegistrationWrapper< PointSource, PointTarget > | |

pcl::apps::RenderViewsTesselatedSphere | Class to render synthetic views of a 3D mesh using a tesselated sphere NOTE: This class should replace renderViewTesselatedSphere from pcl::visualization. Some extensions are planned in the near future to this class like removal of duplicated views for symmetrical objects, generation of RGB synthetic clouds when RGB available on mesh, etc |

pcl::visualization::RenWinInteract | |

pcl::poisson::Octree< Degree >::RestrictedLaplacianMatrixFunction | |

TemplateAlignment::Result | |

pcl::TexMaterial::RGB | |

pcl::RGB | A structure representing RGB color information |

pcl::RGBPlaneCoefficientComparator< PointT, PointNT > | 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 |

pcl::tracking::RGBValue | |

pcl::RIFTEstimation< PointInT, GradientT, PointOutT > | 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: |

pcl::RIFTEstimation< PointInT, GradientT, Eigen::MatrixXf > | RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity. For more information about the RIFT descriptor, see: |

pcl::poisson::RootInfo | |

pcl::RSDEstimation< PointInT, PointNT, PointOutT > | 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 |

pcl::SACSegmentation< PointT > | 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 |

pcl::SACSegmentationFromNormals< PointT, PointNT > | SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation |

pcl::SampleConsensus< T > | SampleConsensus represents the base class. All sample consensus methods must inherit from this class |

pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT > | 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 |

pcl::SampleConsensusModel< PointT > | SampleConsensusModel represents the base model class. All sample consensus models must inherit from this class |

pcl::SampleConsensusModelCircle2D< PointT > | SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane |

pcl::SampleConsensusModelCone< PointT, PointNT > | SampleConsensusModelCone defines a model for 3D cone segmentation. The model coefficients are defined as: |

pcl::SampleConsensusModelCylinder< PointT, PointNT > | SampleConsensusModelCylinder defines a model for 3D cylinder segmentation. The model coefficients are defined as: |

pcl::SampleConsensusModelFromNormals< PointT, PointNT > | SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation |

pcl::SampleConsensusModelLine< PointT > | SampleConsensusModelLine defines a model for 3D line segmentation. The model coefficients are defined as: |

pcl::SampleConsensusModelNormalParallelPlane< PointT, PointNT > | 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 |

pcl::SampleConsensusModelNormalPlane< PointT, PointNT > | 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 |

pcl::SampleConsensusModelNormalSphere< PointT, PointNT > | 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 |

pcl::SampleConsensusModelParallelLine< PointT > | SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints. The model coefficients are defined as: |

pcl::SampleConsensusModelParallelPlane< PointT > | 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) |

pcl::SampleConsensusModelPerpendicularPlane< PointT > | 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: |

pcl::SampleConsensusModelPlane< PointT > | SampleConsensusModelPlane defines a model for 3D plane segmentation. The model coefficients are defined as: |

pcl::SampleConsensusModelRegistration< PointT > | SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection |

pcl::SampleConsensusModelSphere< PointT > | SampleConsensusModelSphere defines a model for 3D sphere segmentation. The model coefficients are defined as: |

pcl::SampleConsensusModelStick< PointT > | 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: |

pcl::io::ply::ply_parser::scalar_property< ScalarType > | |

pcl::io::ply::ply_parser::scalar_property_callback_type< ScalarType > | |

pcl::io::ply::ply_parser::scalar_property_definition_callback_type< ScalarType > | |

pcl::io::ply::ply_parser::scalar_property_definition_callbacks_type | |

ScanParameters | |

pcl::ScopeTime | Class to measure the time spent in a scope |

pcl::search::Search< PointT > | Generic search class. All search wrappers must inherit from this |

pcl::SegmentDifferences< PointT > | SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold |

pcl::io::ply::ply_parser::list_property_definition_callbacks_type::sequence_product< Sequence1, Sequence2 > | |

pcl::SetIfFieldExists< PointOutT, InT > | A helper functor that can set a specific value in a field if the field exists |

pcl::RangeImageBorderExtractor::ShadowBorderIndices | Stores the indices of the shadow border corresponding to obstacle borders |

pcl::ShapeContext | A point structure representing a Shape Context |

pcl::ShapeContext3DEstimation< PointInT, PointNT, PointOutT > | ShapeContext3DEstimation implements the 3D shape context descriptor as described in: |

pcl::ShapeContext3DEstimation< PointInT, PointNT, Eigen::MatrixXf > | ShapeContext3DEstimation implements the 3D shape context descriptor as described in: |

pcl::SHOT | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) |

pcl::SHOT1344 | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape+color |

pcl::SHOT352 | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape only |

pcl::SHOTColorEstimation< PointInT, PointNT, PointOutT, PointRFT > | SHOTColorEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors |

pcl::SHOTColorEstimation< PointInT, PointNT, Eigen::MatrixXf, PointRFT > | |

pcl::SHOTColorEstimationOMP< PointInT, PointNT, PointOutT, PointRFT > | |

pcl::SHOTEstimation< PointInT, PointNT, PointOutT, PointRFT > | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |

pcl::SHOTEstimation< PointInT, PointNT, Eigen::MatrixXf, PointRFT > | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |

pcl::SHOTEstimationBase< PointInT, PointNT, PointOutT, PointRFT > | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |

pcl::SHOTEstimationOMP< PointInT, PointNT, PointOutT, PointRFT > | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |

pcl::SHOTLocalReferenceFrameEstimation< PointInT, PointOutT > | SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor |

pcl::SHOTLocalReferenceFrameEstimationOMP< PointInT, PointOutT > | SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor |

pcl::SIFTKeypoint< PointInT, PointOutT > | 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 |

pcl::SIFTKeypointFieldSelector< PointT > | |

pcl::SIFTKeypointFieldSelector< PointNormal > | |

pcl::SIFTKeypointFieldSelector< PointXYZ > | |

pcl::SIFTKeypointFieldSelector< PointXYZRGB > | |

pcl::SIFTKeypointFieldSelector< PointXYZRGBA > | |

SimpleONIViewer< PointType > | |

SimpleOpenNIProcessor | |

SimpleOpenNIViewer< PointType > | |

pcl::surface::SimplificationRemoveUnusedVertices | |

pcl::SmoothedSurfacesKeypoint< PointT, PointNT > | 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 |

pcl::SolverDidntConvergeException | An exception that is thrown when the non linear solver didn't converge |

pcl::registration::sortCorrespondencesByDistance | |

pcl::registration::sortCorrespondencesByMatchIndex | |

pcl::registration::sortCorrespondencesByMatchIndexAndDistance | |

pcl::registration::sortCorrespondencesByQueryIndex | |

pcl::registration::sortCorrespondencesByQueryIndexAndDistance | |

pcl::poisson::SortedTreeNodes | |

pcl::poisson::SparseMatrix< T > | |

pcl::poisson::SparseNMatrix< T, Dim > | |

pcl::poisson::SparseSymmetricMatrix< T > | |

pcl::SpinImageEstimation< PointInT, PointNT, PointOutT > | Estimates spin-image descriptors in the given input points |

pcl::SpinImageEstimation< PointInT, PointNT, Eigen::MatrixXf > | Estimates spin-image descriptors in the given input points |

pcl::poisson::Square | |

pcl::poisson::StartingPolynomial< Degree > | |

pcl::StaticRangeCoder | StaticRangeCoder compression class |

pcl::StatisticalMultiscaleInterestRegionExtraction< PointT > | 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 |

pcl::StatisticalOutlierRemoval< PointT > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |

pcl::StatisticalOutlierRemoval< sensor_msgs::PointCloud2 > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check: |

pcl::StopWatch | Simple stopwatch |

pcl::SurfaceReconstruction< PointInT > | 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 |

pcl::SurfelSmoothing< PointT, PointNT > | |

pcl::Synchronizer< T1, T2 > | |

pcl::io::TARHeader | A TAR file's header, as described on http://en.wikipedia.org/wiki/Tar_%28file_format%29 |

TemplateAlignment | |

pcl::TexMaterial | |

pcl::TextureMapping< PointInT > | The texture mapping algorithm |

pcl::TextureMesh | |

pcl::TfQuadraticXYZComparison< PointT > | 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 |

pcl::console::TicToc | |

pcl::TimeTrigger | Timer class that invokes registered callback methods periodically |

pcl::tracking::Tracker< PointInT, StateT > | Tracker represents the base tracker class |

pcl::registration::TransformationEstimation< PointSource, PointTarget > | TransformationEstimation represents the base class for methods for transformation estimation based on: |

pcl::registration::TransformationEstimationLM< PointSource, PointTarget > | |

pcl::registration::TransformationEstimationPointToPlane< PointSource, PointTarget > | |

pcl::registration::TransformationEstimationPointToPlaneLLS< PointSource, PointTarget > | TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals |

pcl::registration::TransformationEstimationSVD< PointSource, PointTarget > | |

pcl::TransformationFromCorrespondences | Calculates a transformation based on corresponding 3D points |

pcl::registration::TransformationValidation< PointSource, PointTarget > | TransformationValidation represents the base class for methods that validate the correctness of a transformation found through TransformationEstimation |

pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget > | TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset |

pcl::poisson::TreeNodeData | |

pcl::poisson::Triangle | |

pcl::poisson::TriangleIndex | |

pcl::poisson::Triangulation< Real > | |

pcl::poisson::TriangulationEdge | |

pcl::poisson::TriangulationTriangle | |

pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::TruncatedError | |

pcl::UnhandledPointTypeException | |

pcl::UniformSampling< PointInT > | UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |

pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT > | UniqueShapeContext implements the Unique Shape Descriptor described here: |

pcl::UniqueShapeContext< PointInT, Eigen::MatrixXf, PointRFT > | UniqueShapeContext implements the Unique Shape Descriptor described here: |

pcl::UnorganizedPointCloudException | An exception that is thrown when an organized point cloud is needed but not provided |

pcl::texture_mapping::UvIndex | Structure that links a uv coordinate to its 3D point and face |

pcl::poisson::Vector< T > | |

pcl::VectorAverage< real, dimension > | Calculates the weighted average and the covariance matrix |

pcl::registration::ELCH< PointT >::Vertex | |

pcl::poisson::VertexData | |

pcl::VFHClassifierNN | Utility class for nearest neighbor search based classification of VFH features |

pcl::VFHEstimation< PointInT, PointNT, PointOutT > | 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 |

pcl::VFHSignature308 | A point structure representing the Viewpoint Feature Histogram (VFH) |

pcl::VoxelGrid< PointT > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |

pcl::VoxelGrid< sensor_msgs::PointCloud2 > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |

pcl::VTKUtils | |

pcl::WarpPointRigid< PointSourceT, PointTargetT > | |

pcl::WarpPointRigid3D< PointSourceT, PointTargetT > | |

pcl::WarpPointRigid6D< PointSourceT, PointTargetT > | |

pcl::visualization::Window | |

pcl::xNdCopyEigenPointFunctor< PointT > | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |

pcl::xNdCopyPointEigenFunctor< PointT > | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |