Point Cloud Library (PCL)  1.3.1
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
pcl::_PointWithViewpoint
pcl::_PointXYZ
pcl::_PointXYZHSV
pcl::_PointXYZRGB
pcl::_PointXYZRGBAA point structure representing Euclidean xyz coordinates, and the RGBA color
pcl::_PointXYZRGBL
pcl::_PointXYZRGBNormalA point structure representing Euclidean xyz coordinates, and the RGB color, together with normal coordinates and the surface curvature estimate
pcl::AdaptiveRangeCoderAdaptiveRangeCoder compression class
pcl::ApproximateVoxelGridApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::traits::asEnum
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
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::search::AutotunedSearchsearch::AutotunedSearch is a wrapper class which inherits all the search functions written in PCL and provides an intutive interface to all the functions
pcl::BilateralFilterA bilateral filter implementation for point cloud data
pcl::BivariatePolynomialTThis represents a bivariate polynomial and provides some functionality for it
pcl::BorderDescriptionA structure to store if a point in a range image lies on a border between an obstacle and the background
pcl::BoundaryA point structure representing a description of whether a point is lying on a surface boundary or not
pcl::BoundaryEstimationBoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion
pcl::io::ply::cameraWrapper for PLY camera structure to ease read/write
pcl::visualization::CameraCamera class holds a set of camera parameters together with the window pos/size
pcl::visualization::CloudActor
pcl::visualization::CloudViewerSimple point cloud visualization class
pcl::octree::ColorCodingColorCoding class
pcl::ColorFilter
pcl::ColorFilter< sensor_msgs::PointCloud2 >
pcl::ComparisonBaseThe (abstract) base class for the comparison object
pcl::ConditionalRemovalConditionalRemoval filters data that satisfies certain conditions
pcl::ConditionAndAND condition
pcl::ConditionBaseBase condition class
pcl::ConditionOrOR condition
pcl::octree::configurationProfile_t
pcl::CorrespondenceCorrespondence represents a match between two entities (e.g., points, descriptors, etc)
pcl::registration::CorrespondenceEstimationCorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features
pcl::registration::CorrespondenceRejectorCorrespondenceRejector represents the base class for correspondence rejection methods
pcl::registration::CorrespondenceRejectorDistanceCorrespondenceRejectorDistance implements a simple correspondence rejection method based on thresholding the distances between the correspondences
pcl::registration::CorrespondenceRejectorFeaturesCorrespondenceRejectorFeatures implements a correspondence rejection method based on a set of feature descriptors
pcl::registration::CorrespondenceRejectorOneToOneCorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences
pcl::registration::CorrespondenceRejectorSampleConsensusCorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers)
pcl::registration::CorrespondenceRejectorTrimmedCorrespondenceRejectorTrimmed 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::CropBox
pcl::CropBox< sensor_msgs::PointCloud2 >
pcl::CustomPointRepresentationCustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point
pcl::CVFHEstimationCVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing points and normals
pcl::registration::CorrespondenceRejectorDistance::DataContainer
pcl::registration::CorrespondenceRejectorDistance::DataContainerInterface
pcl::traits::datatype
pcl::traits::decomposeArray
pcl::DefaultFeatureRepresentationDefaulFeatureRepresentation 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::DefaultPointRepresentationDefaultPointRepresentation 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< SHOT >
pcl::DefaultPointRepresentation< VFHSignature308 >
pcl::apps::DominantPlaneSegmentationDominantPlaneSegmentation performs euclidean segmentation on a scene assuming that a dominant plane exists
pcl::GreedyProjectionTriangulation::doubleEdgeStruct for storing the edges starting from a fringe point
pcl::EarClippingThe ear clipping triangulation algorithm
pcl::registration::ELCHELCH (Explicit Loop Closing Heuristic) class
pcl::io::ply::element
pcl::utils::details::epsilon< double >
pcl::utils::details::epsilon< float >
pcl::SampleConsensusInitialAlignment::ErrorFunctor
pcl::EuclideanClusterExtractionEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense
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::RangeImage::ExtractedPlaneHelper struct to return the results of a plane extraction
pcl::ExtractIndicesExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud
pcl::ExtractIndices< sensor_msgs::PointCloud2 >ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud
pcl::ExtractPolygonalPrismDataExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given height, generates a 3D polygonal prism
pcl::FeatureFeature represents the base feature class
pcl::registration::CorrespondenceRejectorFeatures::FeatureContainerAn inner class containing pointers to the source and target feature clouds and the parameters needed to perform the correspondence search
pcl::registration::CorrespondenceRejectorFeatures::FeatureContainerInterface
pcl::FeatureFromNormals
pcl::Narf::FeaturePointRepresentation
pcl::detail::FieldAdder
pcl::FieldComparisonThe field-based specialization of the comparison object
pcl::traits::fieldList
pcl::detail::FieldMapper
pcl::detail::FieldMapping
pcl::FileReaderPoint Cloud Data (FILE) file format reader interface
pcl::FileWriterPoint Cloud Data (FILE) file format writer
pcl::FilterFilter represents the base filter class
pcl::Filter< sensor_msgs::PointCloud2 >Filter represents the base filter class
pcl::FilterIndicesFilter represents the base filter class
pcl::FilterIndices< sensor_msgs::PointCloud2 >FilterIndices represents the base filter with indices class
pcl::visualization::FloatImageUtilsProvide some gerneral functionalities regarding 2d float arrays, e.g., for visualization purposes
pcl::for_each_type_impl
pcl::for_each_type_impl< false >
pcl::FPFHEstimationFPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals
pcl::FPFHEstimationOMPFPFHEstimationOMP 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::FPFHSignature33A point structure representing the Signature of Histograms of OrienTations (SHOT)
pcl::visualization::FPSCallback
pcl::registration::TransformationEstimationLM::FunctorGeneric functor for the optimization
pcl::FunctorBase functor all the models that need non linear optimization must define their own one and implement operator() (const Eigen::VectorXd& x, Eigen::VectorXd& fvec) or operator() (const Eigen::VectorXf& x, Eigen::VectorXf& fvec) dependening on the choosen _Scalar
pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al
pcl::GreedyProjectionTriangulationGreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections
pcl::GridProjectionGrid projection surface reconstruction method
pcl::HarrisKeypoint3D
pcl::PPFHashMapSearch::HashKeyStructData structure to hold the information for the key in the feature hash map of the PPFHashMapSearch class
pcl::he
std_msgs::Header
pcl::DefaultFeatureRepresentation::NdCopyPointFunctor::Helper
pcl::DefaultFeatureRepresentation::NdCopyPointFunctor::Helper< Key, FieldT[NrDims], NrDims >
pcl::HistogramA point structure representing an N-D histogram
pcl::SampleConsensusInitialAlignment::HuberPenalty
sensor_msgs::Image
pcl::visualization::ImageViewer
pcl::registration::IncrementalRegistration
pcl::DefaultFeatureRepresentation::IncrementFunctor
pcl::IntegralImage2DGeneric implementation for creating 2D integral images (including second order integral images)
pcl::IntegralImage2DimDetermines an integral image representation for a given organized data array
pcl::IntegralImageNormalEstimationSurface normal estimation on dense data using integral images
pcl::IntegralImageTypeTraits
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::IntensityGradientA point structure representing the intensity gradient of an XYZI point cloud
pcl::IntensityGradientEstimationIntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values
pcl::IntensitySpinEstimationIntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity
pcl::InterestPointA point structure representing an interest point with Euclidean xyz coordinates, and an interest value
pcl::intersect
pcl::InvalidConversionExceptionAn exception that is thrown when a PointCloud2 message cannot be converted into a PCL type
pcl::InvalidSACModelTypeExceptionAn exception that is thrown when a sample consensus model doesn't have the correct number of samples defined in model_types.h
pcl::IOExceptionAn exception that is thrown during an IO error (typical read/write errors)
openni_wrapper::IRImageClass containing just a reference to IR meta data
pcl::PosesFromMatches::PoseEstimate::IsBetter
pcl::IsNotDenseExceptionAn exception that is thrown when a PointCloud is not dense but is attemped to be used as dense
pcl::IterativeClosestPointIterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm
pcl::IterativeClosestPointNonLinearIterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend
pcl::search::KdTreesearch::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure
pcl::KdTreeKdTree represents the base spatial locator class for nearest neighbor estimation
pcl::KdTreeFLANNKdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures
pcl::visualization::KeyboardEvent/brief Class representing key hit/release events
pcl::KeypointKeypoint represents the base class for key points
pcl::LabeledEuclideanClusterExtractionLabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info
pcl::VoxelGrid::LeafSimple structure to hold an nD centroid and the number of points in a leaf
pcl::VoxelGrid< sensor_msgs::PointCloud2 >::LeafSimple structure to hold an nD centroid and the number of points in a leaf
pcl::UniformSampling::LeafSimple structure to hold an nD centroid and the number of points in a leaf
pcl::GridProjection::LeafData leaf
pcl::MarchingCubes::LeafSimple structure to hold a voxel
pcl::LeastMedianSquaresLeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm
pcl::io::ply::list_property
pcl::RangeImageBorderExtractor::LocalSurfaceStores some information extracted from the neighborhood of a point
pcl::MarchingCubesThe marching cubes surface reconstruction algorithm
pcl::MarchingCubesGreedyThe marching cubes surface reconstruction algorithm, using a "greedy" voxelization algorithm
pcl::MarchingCubesGreedyDotThe marching cubes surface reconstruction algorithm, using a "greedy" voxelization algorithm combined with a dot product, to remove the double surface effect
pcl::MaximumLikelihoodSampleConsensusMaximumLikelihoodSampleConsensus 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
pcl::MEstimatorSampleConsensusMEstimatorSampleConsensus 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
pcl::ModelCoefficients
pcl::MomentInvariantsA point structure representing the three moment invariants
pcl::MomentInvariantsEstimationMomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point
pcl::visualization::MouseEvent
pcl::MovingLeastSquaresMovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation
pcl::MultiscaleFeaturePersistenceGeneric 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
pcl::traits::name
pcl::NarfNARF (Normal Aligned Radial Features) is a point feature descriptor type for 3D data
pcl::Narf36A point structure representing the Narf descriptor
pcl::NarfDescriptorComputes NARF feature descriptors for points in a range image
pcl::NarfKeypointNARF (Normal Aligned Radial Feature) keypoints
pcl::NdCentroidFunctorHelper functor structure for n-D centroid estimation
pcl::NdConcatenateFunctorHelper functor structure for concatenate
pcl::NdCopyEigenPointFunctorHelper functor structure for copying data between an Eigen::VectorXf and a PointT
pcl::NdCopyPointEigenFunctorHelper functor structure for copying data between an Eigen::VectorXf and a PointT
pcl::DefaultFeatureRepresentation::NdCopyPointFunctor
pcl::GreedyProjectionTriangulation::nnAngleStruct for storing the angles to nearest neighbors
pcl::NNClassificationNearest neighbor search based classification of PCL point type features
pcl::NormalA point structure representing normal coordinates and the surface curvature estimate
pcl::NormalBasedSignature12A point structure representing the Normal Based Signature for a feature matrix of 4-by-3
pcl::NormalBasedSignatureEstimationNormal-based feature signature estimation class
pcl::NormalEstimationNormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures
pcl::NormalEstimationOMPNormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard
pcl::search::Octreesearch::Octree is a wrapper class which implements nearest neighbor search operations based on the pcl::octree::Octree structure
pcl::octree::Octree2BufBaseOctree double buffer class
pcl::octree::OctreeBaseOctree class
pcl::octree::Octree2BufBase::OctreeBranchOctree branch class
pcl::octree::OctreeBase::OctreeBranchOctree branch class
pcl::octree::OctreeLowMemBase::OctreeBranchOctree branch class
pcl::octree::Octree2BufBase::OctreeKeyOctree key class
pcl::octree::OctreeBase::OctreeKeyOctree key class
pcl::octree::OctreeLowMemBase::OctreeKeyOctree key class
pcl::octree::OctreeLeafAbstractAbstract octree leaf class
pcl::octree::OctreeLeafDataTOctree leaf class that does store a single DataT element
pcl::octree::OctreeLeafDataTVectorOctree leaf class that does store a vector of DataT elements
pcl::octree::OctreeLeafEmptyOctree leaf class that does not store any information
pcl::octree::OctreeLeafNodeIteratorOctree leaf node iterator class
pcl::octree::OctreeLowMemBaseOctree class
pcl::octree::OctreeNodeAbstract octree node class
pcl::octree::OctreeNodeIteratorOctree iterator class
pcl::octree::OctreePointCloudOctree pointcloud class
pcl::octree::OctreePointCloudChangeDetectorOctree pointcloud change detector class
pcl::octree::OctreePointCloudDensityOctree pointcloud density class
pcl::octree::OctreePointCloudDensityLeafOctree pointcloud density leaf node class
pcl::octree::OctreePointCloudOccupancyOctree pointcloud occupancy class
pcl::octree::OctreePointCloudPointVectorOctree pointcloud point vector class
pcl::octree::OctreePointCloudSearchOctree pointcloud search class
pcl::octree::OctreePointCloudSinglePointOctree pointcloud single point class
pcl::octree::OctreePointCloudVoxelCentroidOctree pointcloud voxel centroid class
pcl::traits::offset
pcl::registration::TransformationEstimationLM::OptimizationFunctor
pcl::SampleConsensusModelCircle2D::OptimizationFunctorFunctor for the optimization function
pcl::SampleConsensusModelCylinder::OptimizationFunctorFunctor for the optimization function
pcl::SampleConsensusModelSphere::OptimizationFunctor
pcl::registration::TransformationEstimationLM::OptimizationFunctorWithIndices
pcl::OrganizedFastMeshSimple triangulation/surface reconstruction for organized point clouds
pcl::search::OrganizedNeighborOrgfanizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds
pcl::PackedHSIComparisonA packed HSI specialization of the comparison object
pcl::PackedRGBComparisonA packed rgb specialization of the comparison object
pcl::NarfKeypoint::ParametersParameters used in this class
pcl::NarfDescriptor::Parameters
pcl::PolynomialCalculationsT::ParametersParameters used in this class
pcl::RangeImageBorderExtractor::ParametersParameters used in this class
pcl::PosesFromMatches::ParametersParameters used in this class
pcl::io::ply::parser
pcl::PassThroughPassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
pcl::PassThrough< sensor_msgs::PointCloud2 >PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
pcl::PCAPrincipal Component analysis (PCA) class
pcl::PCDReaderPoint Cloud Data (PCD) file format reader
pcl::PCDWriterPoint Cloud Data (PCD) file format writer
pcl::PCLBasePCL base class
pcl::PCLBase< sensor_msgs::PointCloud2 >
pcl::PCLExceptionA base class for all pcl exceptions which inherits from std::runtime_error
pcl::visualization::PCLHistogramVisualizerPCL histogram visualizer main class
pcl::visualization::PCLHistogramVisualizerInteractorStylePCL histogram visualizer interactory style class
pcl::PCLIOException/brief /ingroup io
pcl::visualization::PCLVisualizerPCL Visualizer main class
pcl::visualization::PCLVisualizerInteractorThe PCLVisualizer interactor
pcl::visualization::PCLVisualizerInteractorStylePCL Visualizer interactory style class
pcl::PFHEstimationPFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals
pcl::PFHRGBEstimation
pcl::PFHRGBSignature250A point structure representing the Point Feature Histogram with colors (PFHRGB)
pcl::PFHSignature125A point structure representing the Point Feature Histogram (PFH)
pcl::PiecewiseLinearFunctionThis provides functionalities to efficiently return values for piecewise linear function
pcl::PLYReaderPoint Cloud Data (PLY) file format reader
pcl::PLYWriterPoint Cloud Data (PLY) file format writer
pcl::traits::POD
pcl::PointCloudPointCloud represents a templated PointCloud implementation
sensor_msgs::PointCloud2
pcl::visualization::PointCloudColorHandlerBase Handler class for PointCloud colors
pcl::visualization::PointCloudColorHandler< sensor_msgs::PointCloud2 >Base Handler class for PointCloud colors
pcl::visualization::PointCloudColorHandlerCustomHandler for predefined user colors
pcl::visualization::PointCloudColorHandlerCustom< sensor_msgs::PointCloud2 >Handler for predefined user colors
pcl::visualization::PointCloudColorHandlerGenericFieldGeneric field handler class for colors
pcl::visualization::PointCloudColorHandlerGenericField< sensor_msgs::PointCloud2 >Generic field handler class for colors
pcl::visualization::PointCloudColorHandlerRandomHandler 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::PointCloudColorHandlerRGBFieldRGB handler class for colors
pcl::visualization::PointCloudColorHandlerRGBField< sensor_msgs::PointCloud2 >RGB handler class for colors
pcl::octree::PointCloudCompressionOctree pointcloud compression class
pcl::visualization::PointCloudGeometryHandlerBase handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandler< sensor_msgs::PointCloud2 >Base handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerCustomCustom handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerCustom< sensor_msgs::PointCloud2 >Custom handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerSurfaceNormalSurface normal handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< sensor_msgs::PointCloud2 >Surface normal handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerXYZXYZ handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerXYZ< sensor_msgs::PointCloud2 >XYZ handler class for PointCloud geometry
pcl::octree::PointCodingPointCoding class
pcl::PointCorrespondenceRepresentation of a (possible) correspondence between two points in two different coordinate frames (e.g
pcl::PointCorrespondence3DRepresentation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g
pcl::PointCorrespondence6DRepresentation of a (possible) correspondence between two points (e.g
pcl::PointDataAtOffsetA datatype that enables type-correct comparisons
sensor_msgs::PointField
pcl::PointIndices
pcl::PointNormalA point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate
pcl::visualization::PointPickingCallback
pcl::visualization::PointPickingEvent/brief Class representing 3D point picking events
pcl::PointRepresentationPointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector
pcl::PointSurfelA 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::PointWithRangeA point structure representing Euclidean xyz coordinates, padded with an extra range float
pcl::PointWithScaleA point structure representing a 3-D position and scale
pcl::PointWithViewpointA point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen
pcl::PointXYA 2D point structure representing Euclidean xy coordinates
pcl::PointXYZA point structure representing Euclidean xyz coordinates
pcl::PointXYZHSV
pcl::PointXYZIA point structure representing Euclidean xyz coordinates, and the intensity value
pcl::PointXYZINormalA point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate
pcl::PointXYZL
pcl::PointXYZRGBA point structure representing Euclidean xyz coordinates, and the RGB color
pcl::PointXYZRGBA
pcl::PointXYZRGBL
pcl::PointXYZRGBNormal
pcl::PolygonMesh
pcl::PolynomialCalculationsTThis provides some functionality for polynomials, like finding roots or approximating bivariate polynomials
pcl::PosesFromMatches::PoseEstimateA result of the pose estimation process
pcl::PosesFromMatchesCalculate 3D transformation based on point correspondencdes
pcl::PPFRegistration::PoseWithVotesStructure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes
pcl::PPFEstimationClass that calculates the "surflet" features for each pair in the given pointcloud
pcl::PPFHashMapSearch
pcl::PPFRegistrationClass that registers two point clouds based on their sets of PPFSignatures
pcl::PPFRGBEstimation
pcl::PPFRGBRegionEstimation
pcl::PPFRGBSignatureA point structure for storing the Point Pair Color Feature (PPFRGB) values
pcl::PPFSignatureA point structure for storing the Point Pair Feature (PPF) values
pcl::PrincipalCurvaturesA point structure representing the principal curvatures and their magnitudes
pcl::PrincipalCurvaturesEstimationPrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals
pcl::PrincipalRadiiRSDA point structure representing the minimum and maximum surface radii (in meters) computed using RSD
pcl::octree::OctreePointCloudSearch::prioBranchQueueEntryPriority queue entry for branch nodes
pcl::octree::OctreePointCloudSearch::prioPointQueueEntryPriority queue entry for point candidates
pcl::ProgressiveSampleConsensusRandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Matching with PROSAC – Progressive Sample Consensus", Chum, O
pcl::ProjectInliersProjectInliers 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::property
pcl::PyramidFeatureHistogramClass that compares two sets of features by using a multiscale representation of the features inside a pyramid
pcl::PyramidFeatureHistogram::PyramidFeatureHistogramLevelStructure for representing a single pyramid histogram level
pcl::RadiusOutlierRemovalRadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K
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::RandomizedMEstimatorSampleConsensusRandomizedMEstimatorSampleConsensus 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::RandomizedRandomSampleConsensusRandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RAndom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O
pcl::RandomSampleRandomSample applies a random sampling with uniform probability
pcl::RandomSample< sensor_msgs::PointCloud2 >RandomSample applies a random sampling with uniform probability
pcl::RandomSampleConsensusRandomSampleConsensus 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
pcl::RangeImageRangeImage is derived from pcl/PointCloud and provides functionalities with focus on situations where a 3D scene was captured from a specific view point
pcl::RangeImageBorderExtractorExtract obstacle borders from range images, meaning positions where there is a transition from foreground to background
pcl::RangeImagePlanarRangeImagePlanar 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::RangeImageVisualizerRange image visualizer class
pcl::RegistrationRegistration represents the base registration class
pcl::RegistrationVisualizerRegistrationVisualizer represents the base class for rendering the intermediate positions ocupied by the source point cloud during it's registration to the target point cloud
pcl::visualization::RenWinInteract
pcl::TexMaterial::RGB
pcl::RGBA structure representing RGB color information
pcl::RIFTEstimationRIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity
pcl::RSDEstimationRSDEstimation 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::SACSegmentationSACSegmentation 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::SACSegmentationFromNormalsSACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation
pcl::SampleConsensusSampleConsensus represents the base class
pcl::SampleConsensusInitialAlignmentSampleConsensusInitialAlignment 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::SampleConsensusModelSampleConsensusModel represents the base model class
pcl::SampleConsensusModelCircle2DSampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane
pcl::SampleConsensusModelCylinderSampleConsensusModelCylinder defines a model for 3D cylinder segmentation
pcl::SampleConsensusModelFromNormalsSampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation
pcl::SampleConsensusModelLineSampleConsensusModelLine defines a model for 3D line segmentation
pcl::SampleConsensusModelNormalParallelPlaneSampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints
pcl::SampleConsensusModelNormalPlaneSampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints
pcl::SampleConsensusModelParallelLineSampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints
pcl::SampleConsensusModelParallelPlaneSampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints
pcl::SampleConsensusModelPerpendicularPlaneSampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints
pcl::SampleConsensusModelPlaneSampleConsensusModelPlane defines a model for 3D plane segmentation
pcl::SampleConsensusModelRegistrationSampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection
pcl::SampleConsensusModelSphereSampleConsensusModelSphere defines a model for 3D sphere segmentation
pcl::SampleConsensusModelStickSampleConsensusModelStick defines a model for 3D stick segmentation
pcl::ScopeTimeClass to measure the time spent in a scope
pcl::search::SearchGeneric search class
pcl::SearchPoint
pcl::SegmentDifferencesSegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold
pcl::RangeImageBorderExtractor::ShadowBorderIndicesStores the indices of the shadow border corresponding to obstacle borders
pcl::ShapeContext3DEstimationClass ShapeContext3DEstimation implements the 3D shape context descriptor as described here
pcl::SHOTA point structure representing the generic Signature of Histograms of OrienTations (SHOT)
pcl::SHOTEstimationSHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimation< pcl::PointXYZRGBA, PointNT, PointOutT >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimationBaseSHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimationOMPSHOTEstimation 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::SHOTEstimationOMP< pcl::PointXYZRGBA, PointNT, PointOutT >
pcl::SIFTKeypointSIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity
pcl::SIFTKeypointFieldSelector
pcl::SIFTKeypointFieldSelector< PointNormal >
pcl::SIFTKeypointFieldSelector< PointXYZRGB >
pcl::surface::SimplificationRemoveUnusedVertices
pcl::SmoothedSurfacesKeypointBased 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::registration::sortCorrespondencesByDistancesortCorrespondencesByDistance : a functor for sorting correspondences by distance
pcl::registration::sortCorrespondencesByMatchIndexsortCorrespondencesByMatchIndex : a functor for sorting correspondences by match index
pcl::registration::sortCorrespondencesByMatchIndexAndDistancesortCorrespondencesByMatchIndexAndDistance : a functor for sorting correspondences by match index _and_ distance
pcl::registration::sortCorrespondencesByQueryIndexsortCorrespondencesByQueryIndex : a functor for sorting correspondences by query index
pcl::registration::sortCorrespondencesByQueryIndexAndDistancesortCorrespondencesByQueryIndexAndDistance : a functor for sorting correspondences by query index _and_ distance
pcl::SpinImageEstimationEstimates spin-image descriptors in the given input points
pcl::StaticRangeCoderStaticRangeCoder compression class
pcl::StatisticalMultiscaleInterestRegionExtractionClass for extracting interest regions from unstructured point clouds, based on a multi scale statistical approach
pcl::StatisticalOutlierRemovalStatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data
pcl::StatisticalOutlierRemoval< sensor_msgs::PointCloud2 >StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data
pcl::StopWatchSimple stopwatch
pcl::SurfaceReconstructionSurfaceReconstruction represents the base surface reconstruction class
pcl::SurfelSmoothing
pcl::Synchronizer/brief This template class synchronizes two data streams of different types
pcl::TexMaterial
pcl::TextureMappingThe texture mapping algorithm
pcl::TextureMesh
pcl::console::TicToc
pcl::TimeTriggerTimer class that invokes registered callback methods periodically
pcl::registration::TransformationEstimationTransformationEstimation represents the base class for methods for transformation estimation based on:
pcl::registration::TransformationEstimationLMTransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences
pcl::registration::TransformationEstimationPointToPlaneTransformationEstimationPointToPlane uses Levenberg Marquardt optimization to find the transformation that minimizes the point-to-plane distance between the given correspondences
pcl::registration::TransformationEstimationPointToPlaneLLSTransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals
pcl::registration::TransformationEstimationSVDTransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences
pcl::TransformationFromCorrespondencesCalculates a transformation based on corresponding 3D points
pcl::SampleConsensusInitialAlignment::TruncatedError
pcl::UniformSamplingUniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::UniqueShapeContextClass UniqueShapeContext implements the unique shape descriptor described here
pcl::VectorAverageCalculates the weighted average and the covariance matrix
pcl::VerticesDescribes a set of vertices in a polygon mesh, by basically storing an array of indices
pcl::VFHClassifierNNUtility class for nearest neighbor search based classification of VFH features
pcl::VFHEstimationVFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals
pcl::VFHSignature308A point structure representing the Viewpoint Feature Histogram (VFH)
pcl::VoxelGridVoxelGrid 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::surface::VTKSmootherVTKSmoother is a wrapper around some subdivision and filter methods from VTK
pcl::WarpPointRigid
pcl::WarpPointRigid3D
pcl::WarpPointRigid6D
pcl::visualization::Window
pcl::xNdCopyEigenPointFunctorHelper functor structure for copying data between an Eigen::VectorXf and a PointT
pcl::xNdCopyPointEigenFunctorHelper functor structure for copying data between an Eigen::VectorXf and a PointT
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