pcl::_PointWithViewpoint | |
pcl::_PointXYZ | |
pcl::_PointXYZHSV | |
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 |
pcl::AdaptiveRangeCoder | AdaptiveRangeCoder compression class |
pcl::ApproximateVoxelGrid | ApproximateVoxelGrid 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::AutotunedSearch | search::AutotunedSearch is a wrapper class which inherits all the search functions written in PCL and provides an intutive interface to all the functions |
pcl::BilateralFilter | A bilateral filter implementation for point cloud data |
pcl::BivariatePolynomialT | 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 | BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion |
pcl::io::ply::camera | Wrapper for PLY camera structure to ease read/write |
pcl::visualization::Camera | Camera class holds a set of camera parameters together with the window pos/size |
pcl::visualization::CloudActor | |
pcl::visualization::CloudViewer | Simple point cloud visualization class |
pcl::octree::ColorCoding | ColorCoding class |
pcl::ColorFilter | |
pcl::ColorFilter< sensor_msgs::PointCloud2 > | |
pcl::ComparisonBase | The (abstract) base class for the comparison object |
pcl::ConditionalRemoval | ConditionalRemoval filters data that satisfies certain conditions |
pcl::ConditionAnd | AND condition |
pcl::ConditionBase | Base condition class |
pcl::ConditionOr | OR condition |
pcl::octree::configurationProfile_t | |
pcl::Correspondence | Correspondence represents a match between two entities (e.g., points, descriptors, etc) |
pcl::registration::CorrespondenceEstimation | CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features |
pcl::registration::CorrespondenceRejector | CorrespondenceRejector represents the base class for correspondence rejection methods |
pcl::registration::CorrespondenceRejectorDistance | CorrespondenceRejectorDistance implements a simple correspondence rejection method based on thresholding the distances between the correspondences |
pcl::registration::CorrespondenceRejectorFeatures | CorrespondenceRejectorFeatures implements a correspondence rejection method based on a set of feature descriptors |
pcl::registration::CorrespondenceRejectorOneToOne | CorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences |
pcl::registration::CorrespondenceRejectorSampleConsensus | CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers) |
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::CropBox | |
pcl::CropBox< sensor_msgs::PointCloud2 > | |
pcl::CustomPointRepresentation | CustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point |
pcl::CVFHEstimation | CVFHEstimation 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::DefaultFeatureRepresentation | DefaulFeatureRepresentation extends PointRepresentation and is intended to be used when defining the default behavior for feature descriptor types (i.e., copy each element of each field into a float array) |
pcl::DefaultPointRepresentation | 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< SHOT > | |
pcl::DefaultPointRepresentation< VFHSignature308 > | |
pcl::apps::DominantPlaneSegmentation | DominantPlaneSegmentation performs euclidean segmentation on a scene assuming that a dominant plane exists |
pcl::GreedyProjectionTriangulation::doubleEdge | Struct for storing the edges starting from a fringe point |
pcl::EarClipping | The ear clipping triangulation algorithm |
pcl::registration::ELCH | ELCH (Explicit Loop Closing Heuristic) class |
pcl::io::ply::element | |
pcl::utils::details::epsilon< double > | |
pcl::utils::details::epsilon< float > | |
pcl::SampleConsensusInitialAlignment::ErrorFunctor | |
pcl::EuclideanClusterExtraction | EuclideanClusterExtraction 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::ExtractedPlane | Helper struct to return the results of a plane extraction |
pcl::ExtractIndices | 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 | 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 | Feature represents the base feature class |
pcl::registration::CorrespondenceRejectorFeatures::FeatureContainer | 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 | |
pcl::Narf::FeaturePointRepresentation | |
pcl::detail::FieldAdder | |
pcl::FieldComparison | The field-based specialization of the comparison object |
pcl::traits::fieldList | |
pcl::detail::FieldMapper | |
pcl::detail::FieldMapping | |
pcl::FileReader | Point Cloud Data (FILE) file format reader interface |
pcl::FileWriter | Point Cloud Data (FILE) file format writer |
pcl::Filter | Filter represents the base filter class |
pcl::Filter< sensor_msgs::PointCloud2 > | Filter represents the base filter class |
pcl::FilterIndices | Filter represents the base filter class |
pcl::FilterIndices< sensor_msgs::PointCloud2 > | FilterIndices represents the base filter with indices class |
pcl::visualization::FloatImageUtils | Provide some gerneral functionalities regarding 2d float arrays, e.g., for visualization purposes |
pcl::for_each_type_impl | |
pcl::for_each_type_impl< false > | |
pcl::FPFHEstimation | FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals |
pcl::FPFHEstimationOMP | FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |
pcl::FPFHSignature33 | A point structure representing the Signature of Histograms of OrienTations (SHOT) |
pcl::visualization::FPSCallback | |
pcl::registration::TransformationEstimationLM::Functor | Generic functor for the optimization |
pcl::Functor | Base 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::GreedyProjectionTriangulation | GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections |
pcl::GridProjection | Grid projection surface reconstruction method |
pcl::HarrisKeypoint3D | |
pcl::PPFHashMapSearch::HashKeyStruct | Data 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::Histogram | A point structure representing an N-D histogram |
pcl::SampleConsensusInitialAlignment::HuberPenalty | |
sensor_msgs::Image | |
pcl::visualization::ImageViewer | |
pcl::registration::IncrementalRegistration | |
pcl::DefaultFeatureRepresentation::IncrementFunctor | |
pcl::IntegralImage2D | Generic implementation for creating 2D integral images (including second order integral images) |
pcl::IntegralImage2Dim | Determines an integral image representation for a given organized data array |
pcl::IntegralImageNormalEstimation | Surface 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::IntensityGradient | A point structure representing the intensity gradient of an XYZI point cloud |
pcl::IntensityGradientEstimation | IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values |
pcl::IntensitySpinEstimation | IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity |
pcl::InterestPoint | A point structure representing an interest point with Euclidean xyz coordinates, and an interest value |
pcl::intersect | |
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 | IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm |
pcl::IterativeClosestPointNonLinear | IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend |
pcl::search::KdTree | search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure |
pcl::KdTree | KdTree represents the base spatial locator class for nearest neighbor estimation |
pcl::KdTreeFLANN | 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 | Keypoint represents the base class for key points |
pcl::LabeledEuclideanClusterExtraction | LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info |
pcl::VoxelGrid::Leaf | Simple structure to hold an nD centroid and the number of points in a leaf |
pcl::VoxelGrid< sensor_msgs::PointCloud2 >::Leaf | Simple structure to hold an nD centroid and the number of points in a leaf |
pcl::UniformSampling::Leaf | Simple structure to hold an nD centroid and the number of points in a leaf |
pcl::GridProjection::Leaf | Data leaf |
pcl::MarchingCubes::Leaf | Simple structure to hold a voxel |
pcl::LeastMedianSquares | LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm |
pcl::io::ply::list_property | |
pcl::RangeImageBorderExtractor::LocalSurface | Stores some information extracted from the neighborhood of a point |
pcl::MarchingCubes | The marching cubes surface reconstruction algorithm |
pcl::MarchingCubesGreedy | The marching cubes surface reconstruction algorithm, using a "greedy" voxelization algorithm |
pcl::MarchingCubesGreedyDot | The marching cubes surface reconstruction algorithm, using a "greedy" voxelization algorithm combined with a dot product, to remove the double surface effect |
pcl::MaximumLikelihoodSampleConsensus | MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to
estimating image geometry", P.H.S |
pcl::MEstimatorSampleConsensus | MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S |
pcl::ModelCoefficients | |
pcl::MomentInvariants | A point structure representing the three moment invariants |
pcl::MomentInvariantsEstimation | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |
pcl::visualization::MouseEvent | |
pcl::MovingLeastSquares | MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation |
pcl::MultiscaleFeaturePersistence | Generic class for extracting the persistent features from an input point cloud It can be given any Feature estimator instance and will compute the features of the input over a multiscale representation of the cloud and output the unique ones over those scales |
pcl::traits::name | |
pcl::Narf | NARF (Normal Aligned Radial Features) is a point feature descriptor type for 3D data |
pcl::Narf36 | A point structure representing the Narf descriptor |
pcl::NarfDescriptor | Computes NARF feature descriptors for points in a range image |
pcl::NarfKeypoint | NARF (Normal Aligned Radial Feature) keypoints |
pcl::NdCentroidFunctor | Helper functor structure for n-D centroid estimation |
pcl::NdConcatenateFunctor | Helper functor structure for concatenate |
pcl::NdCopyEigenPointFunctor | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |
pcl::NdCopyPointEigenFunctor | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |
pcl::DefaultFeatureRepresentation::NdCopyPointFunctor | |
pcl::GreedyProjectionTriangulation::nnAngle | Struct for storing the angles to nearest neighbors |
pcl::NNClassification | Nearest neighbor search based classification of PCL point type features |
pcl::Normal | A point structure representing normal coordinates and the surface curvature estimate |
pcl::NormalBasedSignature12 | A point structure representing the Normal Based Signature for a feature matrix of 4-by-3 |
pcl::NormalBasedSignatureEstimation | Normal-based feature signature estimation class |
pcl::NormalEstimation | NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures |
pcl::NormalEstimationOMP | NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard |
pcl::search::Octree | search::Octree is a wrapper class which implements nearest neighbor search operations based on the pcl::octree::Octree structure |
pcl::octree::Octree2BufBase | Octree double buffer class |
pcl::octree::OctreeBase | Octree class |
pcl::octree::Octree2BufBase::OctreeBranch | Octree branch class |
pcl::octree::OctreeBase::OctreeBranch | Octree branch class |
pcl::octree::OctreeLowMemBase::OctreeBranch | Octree branch class |
pcl::octree::Octree2BufBase::OctreeKey | Octree key class |
pcl::octree::OctreeBase::OctreeKey | Octree key class |
pcl::octree::OctreeLowMemBase::OctreeKey | Octree key class |
pcl::octree::OctreeLeafAbstract | Abstract octree leaf class |
pcl::octree::OctreeLeafDataT | Octree leaf class that does store a single DataT element |
pcl::octree::OctreeLeafDataTVector | Octree leaf class that does store a vector of DataT elements |
pcl::octree::OctreeLeafEmpty | Octree leaf class that does not store any information |
pcl::octree::OctreeLeafNodeIterator | Octree leaf node iterator class |
pcl::octree::OctreeLowMemBase | Octree class |
pcl::octree::OctreeNode | Abstract octree node class |
pcl::octree::OctreeNodeIterator | Octree iterator class |
pcl::octree::OctreePointCloud | Octree pointcloud class |
pcl::octree::OctreePointCloudChangeDetector | Octree pointcloud change detector class |
pcl::octree::OctreePointCloudDensity | Octree pointcloud density class |
pcl::octree::OctreePointCloudDensityLeaf | Octree pointcloud density leaf node class |
pcl::octree::OctreePointCloudOccupancy | Octree pointcloud occupancy class |
pcl::octree::OctreePointCloudPointVector | Octree pointcloud point vector class |
pcl::octree::OctreePointCloudSearch | Octree pointcloud search class |
pcl::octree::OctreePointCloudSinglePoint | Octree pointcloud single point class |
pcl::octree::OctreePointCloudVoxelCentroid | Octree pointcloud voxel centroid class |
pcl::traits::offset | |
pcl::registration::TransformationEstimationLM::OptimizationFunctor | |
pcl::SampleConsensusModelCircle2D::OptimizationFunctor | Functor for the optimization function |
pcl::SampleConsensusModelCylinder::OptimizationFunctor | Functor for the optimization function |
pcl::SampleConsensusModelSphere::OptimizationFunctor | |
pcl::registration::TransformationEstimationLM::OptimizationFunctorWithIndices | |
pcl::OrganizedFastMesh | Simple triangulation/surface reconstruction for organized point clouds |
pcl::search::OrganizedNeighbor | OrgfanizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds |
pcl::PackedHSIComparison | A packed HSI specialization of the comparison object |
pcl::PackedRGBComparison | A packed rgb specialization of the comparison object |
pcl::NarfKeypoint::Parameters | Parameters used in this class |
pcl::NarfDescriptor::Parameters | |
pcl::PolynomialCalculationsT::Parameters | Parameters used in this class |
pcl::RangeImageBorderExtractor::Parameters | Parameters used in this class |
pcl::PosesFromMatches::Parameters | Parameters used in this class |
pcl::io::ply::parser | |
pcl::PassThrough | 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 | Principal Component analysis (PCA) class |
pcl::PCDReader | Point Cloud Data (PCD) file format reader |
pcl::PCDWriter | Point Cloud Data (PCD) file format writer |
pcl::PCLBase | PCL base class |
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::PCLIOException | /brief /ingroup io |
pcl::visualization::PCLVisualizer | PCL Visualizer main class |
pcl::visualization::PCLVisualizerInteractor | The PCLVisualizer interactor |
pcl::visualization::PCLVisualizerInteractorStyle | PCL Visualizer interactory style class |
pcl::PFHEstimation | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |
pcl::PFHRGBEstimation | |
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::PLYReader | Point Cloud Data (PLY) file format reader |
pcl::PLYWriter | Point Cloud Data (PLY) file format writer |
pcl::traits::POD | |
pcl::PointCloud | PointCloud represents a templated PointCloud implementation |
sensor_msgs::PointCloud2 | |
pcl::visualization::PointCloudColorHandler | Base Handler class for PointCloud colors |
pcl::visualization::PointCloudColorHandler< sensor_msgs::PointCloud2 > | Base Handler class for PointCloud colors |
pcl::visualization::PointCloudColorHandlerCustom | Handler for predefined user colors |
pcl::visualization::PointCloudColorHandlerCustom< sensor_msgs::PointCloud2 > | Handler for predefined user colors |
pcl::visualization::PointCloudColorHandlerGenericField | Generic field handler class for colors |
pcl::visualization::PointCloudColorHandlerGenericField< sensor_msgs::PointCloud2 > | Generic field handler class for colors |
pcl::visualization::PointCloudColorHandlerRandom | 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 | RGB handler class for colors |
pcl::visualization::PointCloudColorHandlerRGBField< sensor_msgs::PointCloud2 > | RGB handler class for colors |
pcl::octree::PointCloudCompression | Octree pointcloud compression class |
pcl::visualization::PointCloudGeometryHandler | Base handler class for PointCloud geometry |
pcl::visualization::PointCloudGeometryHandler< sensor_msgs::PointCloud2 > | Base handler class for PointCloud geometry |
pcl::visualization::PointCloudGeometryHandlerCustom | Custom handler class for PointCloud geometry |
pcl::visualization::PointCloudGeometryHandlerCustom< sensor_msgs::PointCloud2 > | Custom handler class for PointCloud geometry |
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal | Surface normal handler class for PointCloud geometry |
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< sensor_msgs::PointCloud2 > | Surface normal handler class for PointCloud geometry |
pcl::visualization::PointCloudGeometryHandlerXYZ | XYZ handler class for PointCloud geometry |
pcl::visualization::PointCloudGeometryHandlerXYZ< sensor_msgs::PointCloud2 > | XYZ handler class for PointCloud geometry |
pcl::octree::PointCoding | PointCoding class |
pcl::PointCorrespondence | Representation of a (possible) correspondence between two points in two different coordinate frames (e.g |
pcl::PointCorrespondence3D | Representation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g |
pcl::PointCorrespondence6D | Representation of a (possible) correspondence between two points (e.g |
pcl::PointDataAtOffset | A datatype that enables type-correct comparisons |
sensor_msgs::PointField | |
pcl::PointIndices | |
pcl::PointNormal | A 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 | PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector |
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 | 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 |
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 | A point structure representing Euclidean xyz coordinates, and the RGB color |
pcl::PointXYZRGBA | |
pcl::PointXYZRGBL | |
pcl::PointXYZRGBNormal | |
pcl::PolygonMesh | |
pcl::PolynomialCalculationsT | 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::PoseWithVotes | Structure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes |
pcl::PPFEstimation | Class that calculates the "surflet" features for each pair in the given pointcloud |
pcl::PPFHashMapSearch | |
pcl::PPFRegistration | Class that registers two point clouds based on their sets of PPFSignatures |
pcl::PPFRGBEstimation | |
pcl::PPFRGBRegionEstimation | |
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::PrincipalCurvatures | A point structure representing the principal curvatures and their magnitudes |
pcl::PrincipalCurvaturesEstimation | 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::prioBranchQueueEntry | Priority queue entry for branch nodes |
pcl::octree::OctreePointCloudSearch::prioPointQueueEntry | Priority queue entry for point candidates |
pcl::ProgressiveSampleConsensus | RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Matching with PROSAC – Progressive Sample Consensus", Chum, O |
pcl::ProjectInliers | 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 | Class that compares two sets of features by using a multiscale representation of the features inside a pyramid |
pcl::PyramidFeatureHistogram::PyramidFeatureHistogramLevel | Structure for representing a single pyramid histogram level |
pcl::RadiusOutlierRemoval | 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::RandomizedMEstimatorSampleConsensus | RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus) |
pcl::RandomizedRandomSampleConsensus | RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RAndom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O |
pcl::RandomSample | RandomSample applies a random sampling with uniform probability |
pcl::RandomSample< sensor_msgs::PointCloud2 > | RandomSample applies a random sampling with uniform probability |
pcl::RandomSampleConsensus | RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and
Automated Cartography", Martin A |
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 |
pcl::Registration | Registration represents the base registration class |
pcl::RegistrationVisualizer | RegistrationVisualizer represents the base class for rendering the intermediate positions ocupied by the source point cloud during it's registration to the target point cloud |
pcl::visualization::RenWinInteract | |
pcl::TexMaterial::RGB | |
pcl::RGB | A structure representing RGB color information |
pcl::RIFTEstimation | RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity |
pcl::RSDEstimation | RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves) for a given point cloud dataset containing points and normals |
pcl::SACSegmentation | SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation |
pcl::SACSegmentationFromNormals | SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation |
pcl::SampleConsensus | SampleConsensus represents the base class |
pcl::SampleConsensusInitialAlignment | SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al |
pcl::SampleConsensusModel | SampleConsensusModel represents the base model class |
pcl::SampleConsensusModelCircle2D | SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane |
pcl::SampleConsensusModelCylinder | SampleConsensusModelCylinder defines a model for 3D cylinder segmentation |
pcl::SampleConsensusModelFromNormals | SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation |
pcl::SampleConsensusModelLine | SampleConsensusModelLine defines a model for 3D line segmentation |
pcl::SampleConsensusModelNormalParallelPlane | SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints |
pcl::SampleConsensusModelNormalPlane | SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints |
pcl::SampleConsensusModelParallelLine | SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints |
pcl::SampleConsensusModelParallelPlane | SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints |
pcl::SampleConsensusModelPerpendicularPlane | SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints |
pcl::SampleConsensusModelPlane | SampleConsensusModelPlane defines a model for 3D plane segmentation |
pcl::SampleConsensusModelRegistration | SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection |
pcl::SampleConsensusModelSphere | SampleConsensusModelSphere defines a model for 3D sphere segmentation |
pcl::SampleConsensusModelStick | SampleConsensusModelStick defines a model for 3D stick segmentation |
pcl::ScopeTime | Class to measure the time spent in a scope |
pcl::search::Search | Generic search class |
pcl::SearchPoint | |
pcl::SegmentDifferences | SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold |
pcl::RangeImageBorderExtractor::ShadowBorderIndices | Stores the indices of the shadow border corresponding to obstacle borders |
pcl::ShapeContext3DEstimation | Class ShapeContext3DEstimation implements the 3D shape context descriptor as described here |
pcl::SHOT | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) |
pcl::SHOTEstimation | 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 | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |
pcl::SHOTEstimationOMP | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |
pcl::SHOTEstimationOMP< pcl::PointXYZRGBA, PointNT, PointOutT > | |
pcl::SIFTKeypoint | SIFTKeypoint 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::SmoothedSurfacesKeypoint | Based on the paper: Xinju Li and Igor Guskov Multi-scale features for approximate alignment of point-based surfaces Proceedings of the third Eurographics symposium on Geometry processing July 2005, Vienna, Austria |
pcl::registration::sortCorrespondencesByDistance | sortCorrespondencesByDistance : a functor for sorting correspondences by distance |
pcl::registration::sortCorrespondencesByMatchIndex | sortCorrespondencesByMatchIndex : a functor for sorting correspondences by match index |
pcl::registration::sortCorrespondencesByMatchIndexAndDistance | sortCorrespondencesByMatchIndexAndDistance : a functor for sorting correspondences by match index _and_ distance |
pcl::registration::sortCorrespondencesByQueryIndex | sortCorrespondencesByQueryIndex : a functor for sorting correspondences by query index |
pcl::registration::sortCorrespondencesByQueryIndexAndDistance | sortCorrespondencesByQueryIndexAndDistance : a functor for sorting correspondences by query index _and_ distance |
pcl::SpinImageEstimation | Estimates spin-image descriptors in the given input points |
pcl::StaticRangeCoder | StaticRangeCoder compression class |
pcl::StatisticalMultiscaleInterestRegionExtraction | Class for extracting interest regions from unstructured point clouds, based on a multi scale statistical approach |
pcl::StatisticalOutlierRemoval | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |
pcl::StatisticalOutlierRemoval< sensor_msgs::PointCloud2 > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |
pcl::StopWatch | Simple stopwatch |
pcl::SurfaceReconstruction | SurfaceReconstruction represents the base surface reconstruction class |
pcl::SurfelSmoothing | |
pcl::Synchronizer | /brief This template class synchronizes two data streams of different types |
pcl::TexMaterial | |
pcl::TextureMapping | The texture mapping algorithm |
pcl::TextureMesh | |
pcl::console::TicToc | |
pcl::TimeTrigger | Timer class that invokes registered callback methods periodically |
pcl::registration::TransformationEstimation | TransformationEstimation represents the base class for methods for transformation estimation based on: |
pcl::registration::TransformationEstimationLM | TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences |
pcl::registration::TransformationEstimationPointToPlane | TransformationEstimationPointToPlane uses Levenberg Marquardt optimization to find the transformation that minimizes the point-to-plane distance between the given correspondences |
pcl::registration::TransformationEstimationPointToPlaneLLS | 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 | TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences |
pcl::TransformationFromCorrespondences | Calculates a transformation based on corresponding 3D points |
pcl::SampleConsensusInitialAlignment::TruncatedError | |
pcl::UniformSampling | UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
pcl::UniqueShapeContext | Class UniqueShapeContext implements the unique shape descriptor described here |
pcl::VectorAverage | Calculates the weighted average and the covariance matrix |
pcl::Vertices | Describes a set of vertices in a polygon mesh, by basically storing an array of indices |
pcl::VFHClassifierNN | Utility class for nearest neighbor search based classification of VFH features |
pcl::VFHEstimation | VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals |
pcl::VFHSignature308 | A point structure representing the Viewpoint Feature Histogram (VFH) |
pcl::VoxelGrid | 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::surface::VTKSmoother | VTKSmoother is a wrapper around some subdivision and filter methods from VTK |
pcl::WarpPointRigid | |
pcl::WarpPointRigid3D | |
pcl::WarpPointRigid6D | |
pcl::visualization::Window | |
pcl::xNdCopyEigenPointFunctor | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |
pcl::xNdCopyPointEigenFunctor | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |