Class List

Here are the classes, structs, unions and interfaces with brief descriptions:
pcl::_PointWithViewpoint
pcl::_PointXYZ
pcl::_PointXYZRGB
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::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::BivariatePolynomialT< real >This 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::BoundaryEstimation< PointInT, PointNT, PointOutT >BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion
pcl::visualization::CameraCamera class holds a set of camera parameters together with the window pos/size
pcl::io::ply::cameraWrapper for PLY camera structure to ease read/write
pcl::visualization::CloudActor
pcl::visualization::CloudViewerSimple point cloud visualization class
pcl::octree::ColorCoding< PointT >ColorCoding class
pcl::ComparisonBase< PointT >The (abstract) base class for the comparison object
pcl::ConcaveHull< PointInT >ConcaveHull (alpha shapes) using libqhull library
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
pcl::octree::configurationProfile_t
pcl::ConvexHull< PointInT >ConvexHull using libqhull library
pcl::CorrespondenceCorrespondence represents a match between two entities (e.g., points, descriptors, etc)
pcl::registration::CorrespondenceEstimation< PointSource, PointTarget >CorrespondenceEstimation 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::CorrespondenceRejectorSampleConsensus< PointT >CorrespondenceRejectorSampleConsensus 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::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 points and normals
pcl::registration::CorrespondenceRejectorDistance::DataContainer< PointT >
pcl::registration::CorrespondenceRejectorDistance::DataContainerInterface
pcl::traits::datatype< PointT, Tag >
pcl::traits::decomposeArray< T >
pcl::DefaultPointRepresentation< PointDefault >DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types
pcl::DefaultPointRepresentation< FPFHSignature33 >
pcl::DefaultPointRepresentation< NormalBasedSignature12 >
pcl::DefaultPointRepresentation< PFHSignature125 >
pcl::DefaultPointRepresentation< PointNormal >
pcl::DefaultPointRepresentation< PointXYZ >
pcl::DefaultPointRepresentation< PointXYZI >
pcl::DefaultPointRepresentation< VFHSignature308 >
openni_wrapper::DepthImageThis class provides methods to fill a depth or disparity image
openni_wrapper::OpenNIDriver::DeviceContext
openni_wrapper::DeviceKinectConcrete implementation of the interface OpenNIDevice for a MS Kinect device
openni_wrapper::DeviceONIConcrete implementation of the interface OpenNIDevice for a virtual device playing back an ONI file
openni_wrapper::DevicePrimesenseConcrete implementation of the interface OpenNIDevice for a Primesense device
openni_wrapper::DeviceXtionProConcrete implementation of the interface OpenNIDevice for a Asus Xtion Pro device
pcl::io::ply::element
pcl::EuclideanClusterExtraction< PointT >EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense
pcl::visualization::Window::ExitCallback
pcl::visualization::Window::ExitMainLoopTimerCallback
pcl::ExtractIndices< PointT >ExtractIndices 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::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
pcl::Feature< PointInT, PointOutT >Feature represents the base feature class
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
pcl::registration::CorrespondenceRejectorFeatures::FeatureContainerInterface
pcl::FeatureFromNormals< PointInT, PointNT, PointOutT >
pcl::Narf::FeaturePointRepresentation
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::FileReaderPoint Cloud Data (FILE) file format reader interface
pcl::FileWriterPoint Cloud Data (FILE) file format writer
pcl::Filter< PointT >Filter represents the base filter class
pcl::Filter< sensor_msgs::PointCloud2 >Filter represents the base filter class
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::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::FPFHSignature33A point structure representing the Signature of Histograms of OrienTations (SHOT)
pcl::visualization::FPSCallback
pcl::GrabberGrabber 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
pcl::GridProjection< PointNT >Grid projection surface reconstruction method
std_msgs::Header
pcl::Histogram< N >A point structure representing an N-D histogram
sensor_msgs::Image
openni_wrapper::ImageImage class containing just a reference to image meta data
openni_wrapper::ImageBayerGRBGThis class provides methods to fill a RGB or Grayscale image buffer from underlying Bayer pattern image
openni_wrapper::ImageRGB24This class provides methods to fill a RGB or Grayscale image buffer from underlying RGB24 image
pcl::visualization::ImageViewer
openni_wrapper::ImageYUV422Concrete implementation of the interface Image for a YUV 422 image used by Primesense devices
pcl::IntegralImage2D< DataType, IIDataType >Generic implementation for creating 2D integral images (including second order integral images)
pcl::IntegralImageNormalEstimation< PointInT, PointOutT >Surface normal estimation on dense data using integral images
pcl::IntensityGradientA point structure representing the intensity gradient of an XYZI point cloud
pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT >IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values
pcl::IntensitySpinEstimation< PointInT, PointOutT >IntensitySpinEstimation 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< Sequence1, Sequence2 >
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::IterativeClosestPoint< PointSource, PointTarget >IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm
pcl::IterativeClosestPointNonLinear< PointSource, PointTarget >IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend
pcl::KdTree< PointT >KdTree represents the base spatial locator class for nearest neighbor estimation
pcl::KdTreeFLANN< PointT >KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures
pcl::visualization::KeyboardEvent/brief Class representing key hit/release events
pcl::Keypoint< PointInT, PointOutT >Keypoint represents the base class for key points
pcl::UniformSampling< PointInT >::LeafSimple structure to hold an nD centroid and the number of points in a leaf
pcl::GridProjection< PointNT >::LeafData leaf
pcl::MarchingCubes< PointNT >::LeafSimple structure to hold a voxel
pcl::VoxelGrid< PointT >::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::LeastMedianSquares< PointT >LeastMedianSquares 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::MarchingCubes< PointNT >The marching cubes surface reconstruction algorithm
pcl::MarchingCubesGreedy< PointNT >The marching cubes surface reconstruction algorithm, using a "greedy" voxelization algorithm
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
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
pcl::ModelCoefficients
pcl::MomentInvariantsA 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::visualization::MouseEvent
pcl::MovingLeastSquares< PointInT, NormalOutT >MovingLeastSquares represent an 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
pcl::traits::name< PointT, Tag, dummy >
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::NdCentroidFunctor< PointT >Helper functor structure for n-D centroid estimation
pcl::NdConcatenateFunctor< PointInT, PointOutT >Helper functor structure for concatenate
pcl::NdCopyEigenPointFunctor< PointT >Helper functor structure for copying data between an Eigen::VectorXf and a PointT
pcl::NdCopyPointEigenFunctor< PointT >Helper functor structure for copying data between an Eigen::VectorXf and a PointT
pcl::OrganizedNeighborSearch< PointT >::nearestNeighborCandidatenearestNeighborCandidate entry for the nearest neighbor candidate queue
pcl::NNClassification< PointT >Nearest 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::NormalEstimation< PointInT, PointOutT >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::octree::Octree2BufBase< DataT, LeafT >Octree double buffer class
pcl::octree::OctreeBase< DataT, LeafT >Octree class
pcl::octree::Octree2BufBase< DataT, LeafT >::OctreeBranchOctree branch class
pcl::octree::OctreeBase< DataT, LeafT >::OctreeBranchOctree branch class
pcl::octree::OctreeLowMemBase< DataT, LeafT >::OctreeBranchOctree branch class
pcl::octree::Octree2BufBase< DataT, LeafT >::OctreeKeyOctree key class
pcl::octree::OctreeBase< DataT, LeafT >::OctreeKeyOctree key class
pcl::octree::OctreeLowMemBase< DataT, LeafT >::OctreeKeyOctree key class
pcl::octree::OctreeLeafAbstract< DataT >Abstract octree leaf class
pcl::octree::OctreeLeafDataT< DataT >Octree leaf class that does store a single DataT element
pcl::octree::OctreeLeafDataTVector< DataT >Octree leaf class that does store a vector of DataT elements
pcl::octree::OctreeLeafEmpty< DataT >Octree leaf class that does not store any information
pcl::octree::OctreeLowMemBase< DataT, LeafT >Octree class
pcl::octree::OctreeNodeAbstract octree node class
pcl::octree::OctreePointCloud< PointT, LeafT, OctreeT >Octree pointcloud class
pcl::octree::OctreePointCloudChangeDetector< PointT, LeafT >Octree pointcloud change detector class
pcl::octree::OctreePointCloudDensity< PointT, LeafT, OctreeT >Octree pointcloud density class
pcl::octree::OctreePointCloudDensityLeaf< DataT >Octree pointcloud density leaf node class
pcl::octree::OctreePointCloudOccupancy< PointT, LeafT, OctreeT >Octree pointcloud occupancy class
pcl::octree::OctreePointCloudPointVector< PointT, LeafT, OctreeT >Octree pointcloud point vector class
pcl::octree::OctreePointCloudSinglePoint< PointT, LeafT, OctreeT >Octree pointcloud single point class
pcl::octree::OctreePointCloudVoxelCentroid< PointT, LeafT, OctreeT >Octree pointcloud voxel centroid class
pcl::traits::offset< PointT, Tag >
pcl::ONIGrabber
openni_wrapper::OpenNIDeviceClass representing an astract device for Primesense or MS Kinect devices
openni_wrapper::OpenNIDriverDriver class implemented as Singleton
openni_wrapper::OpenNIExceptionGeneral exception class
pcl::OpenNIGrabber/brief /ingroup io
pcl::OrganizedDataIndex< PointT >OrganizedDataIndex is a type of spatial locator used to query organized datasets, such as point clouds acquired using dense stereo devices
pcl::OrganizedFastMesh< PointInT >Simple triangulation/surface reconstruction for organized point clouds
pcl::OrganizedNeighborSearch< PointT >OrganizedNeighborSearch class
pcl::PackedHSIComparison< PointT >A packed HSI specialization of the comparison object
pcl::PackedRGBComparison< PointT >A packed rgb specialization of the comparison object
pcl::NarfDescriptor::Parameters
pcl::RangeImageBorderExtractor::ParametersParameters used in this class
pcl::PolynomialCalculationsT< real >::ParametersParameters used in this class
pcl::PosesFromMatches::ParametersParameters used in this class
pcl::NarfKeypoint::ParametersParameters used in this class
pcl::io::ply::parser
pcl::PassThrough< PointT >PassThrough 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::PCA< PointT >Principal Component analysis (PCA) class
pcl::PCDGrabber< PointT >
pcl::PCDGrabberBaseBase class for PCD file grabber
pcl::PCDReaderPoint Cloud Data (PCD) file format reader
pcl::PCDWriterPoint Cloud Data (PCD) file format writer
pcl::PCLBase< PointT >PCL 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::PFHEstimation< PointInT, PointNT, PointOutT >PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals
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< PointT >
pcl::PointCloud< PointT >PointCloud represents a templated PointCloud implementation
sensor_msgs::PointCloud2
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
pcl::visualization::PointCloudColorHandlerCustom< sensor_msgs::PointCloud2 >Handler for predefined user colors
pcl::visualization::PointCloudColorHandlerGenericField< PointT >Generic field handler class for colors
pcl::visualization::PointCloudColorHandlerGenericField< sensor_msgs::PointCloud2 >Generic field handler class for colors
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
pcl::visualization::PointCloudColorHandlerRGBField< sensor_msgs::PointCloud2 >RGB handler class for colors
pcl::octree::PointCloudCompression< PointT, LeafT, 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
pcl::visualization::PointCloudGeometryHandlerCustom< sensor_msgs::PointCloud2 >Custom handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< PointT >Surface normal handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< sensor_msgs::PointCloud2 >Surface normal handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerXYZ< PointT >XYZ handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerXYZ< sensor_msgs::PointCloud2 >XYZ handler class for PointCloud geometry
pcl::octree::PointCoding< PointT >PointCoding 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::PointDataAtOffset< PointT >A 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::PointRepresentation< PointT >PointRepresentation 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::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::PointXYZRGBA point structure representing Euclidean xyz coordinates, and the RGB color
pcl::PointXYZRGBAA point structure representing Euclidean xyz coordinates, and the RGBA color
pcl::PointXYZRGBNormal
pcl::PolygonMesh
pcl::PolynomialCalculationsT< real >This 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::PPFEstimation< PointInT, PointNT, PointOutT >Class that calculates the "surflet" features for each pair in the given pointcloud
pcl::PPFSignatureA point structure for storing the Point Pair Feature (PPF) values
pcl::PrincipalCurvaturesA 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::PrincipalRadiiRSDA point structure representing the minimum and maximum surface radii (in meters) computed using RSD
pcl::octree::OctreePointCloud< PointT, LeafT, OctreeT >::prioBranchQueueEntryPriority queue entry for branch nodes
pcl::octree::OctreePointCloud< PointT, LeafT, OctreeT >::prioPointQueueEntryPriority queue entry for point candidates
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
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::property
pcl::PyramidFeatureHistogram< PointFeature >Class that compares two sets of features by using a multiscale representation of the features inside a pyramid
pcl::RadiusOutlierRemoval< PointT >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::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::OrganizedNeighborSearch< PointT >::radiusSearchLoopkupEntryradiusSearchLoopkupEntry entry for radius search lookup vector
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
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
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::Registration< PointSource, PointTarget >Registration represents the base registration 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
pcl::visualization::RenWinInteract
pcl::RGBA structure representing RGB color information
pcl::RIFTEstimation< PointInT, GradientT, PointOutT >RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity
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
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
pcl::SampleConsensusModelCircle2D< PointT >SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane
pcl::SampleConsensusModelCylinder< PointT, PointNT >SampleConsensusModelCylinder defines a model for 3D cylinder segmentation
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
pcl::SampleConsensusModelNormalParallelPlane< PointT, PointNT >SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints
pcl::SampleConsensusModelNormalPlane< PointT, PointNT >SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints
pcl::SampleConsensusModelParallelLine< PointT >SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints
pcl::SampleConsensusModelParallelPlane< PointT >SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints
pcl::SampleConsensusModelPerpendicularPlane< PointT >SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints
pcl::SampleConsensusModelPlane< PointT >SampleConsensusModelPlane defines a model for 3D plane segmentation
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
pcl::SampleConsensusModelStick< PointT >SampleConsensusModelStick defines a model for 3D stick segmentation
pcl::ScopeTimeClass to measure the time spent in a scope
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::RangeImageBorderExtractor::ShadowBorderIndicesStores the indices of the shadow border corresponding to obstacle borders
pcl::SHOTA point structure representing the generic Signature of Histograms of OrienTations (SHOT)
pcl::SHOTEstimation< PointInT, PointNT, PointOutT >SHOTEstimation 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::SHOTEstimationBase< PointInT, PointNT, PointOutT >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 >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::SHOTEstimationOMP< pcl::PointXYZRGBA, PointNT, PointOutT >
pcl::SIFTKeypoint< PointInT, PointOutT >SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity
pcl::SIFTKeypointFieldSelector< PointT >
pcl::SIFTKeypointFieldSelector< PointNormal >
pcl::SIFTKeypointFieldSelector< PointXYZRGB >
pcl::surface::SimplificationRemoveUnusedVertices
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::StaticRangeCoderStaticRangeCoder compression class
pcl::StatisticalMultiscaleInterestRegionExtraction< PointT >
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
pcl::SurfaceReconstruction< PointInT >SurfaceReconstruction represents the base surface reconstruction class
pcl::SurfelSmoothing< PointT, PointNT >
pcl::Synchronizer< T1, T2 >/brief This template class synchronizes two data streams of different types
pcl::TexMaterial
pcl::TextureMesh
pcl::console::TicToc
pcl::TimeTriggerTimer class that invokes registered callback methods periodically
pcl::registration::TransformationEstimation< PointSource, PointTarget >TransformationEstimation represents the base class for methods for transformation estimation based on:
pcl::registration::TransformationEstimationLM< PointSource, PointTarget >TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences
pcl::registration::TransformationEstimationSVD< PointSource, PointTarget >TransformationEstimationSVD implements an SVD-based estimation of the transformation aligning the given correspondences
pcl::TransformationFromCorrespondencesCalculates a transformation based on corresponding 3D points
pcl::UniformSampling< PointInT >UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::VectorAverage< real, dimension >Calculates 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::VFHEstimation< PointInT, PointNT, PointOutT >VFHEstimation 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::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::WarpPointRigid< PointSourceT, PointTargetT >
pcl::WarpPointRigid3D< PointSourceT, PointTargetT >
pcl::WarpPointRigid6D< PointSourceT, PointTargetT >
pcl::visualization::Window