  pcl::_Axis | A point structure representing an Axis using its normal coordinates |
  pcl::Axis | |
| pcl::_Normal | A point structure representing normal coordinates and the surface curvature estimate |
| pcl::Normal | |
| pcl::_PointNormal | A point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate |
| pcl::PointNormal | |
| 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::PointSurfel | |
| pcl::_PointWithRange | A point structure representing Euclidean xyz coordinates, padded with an extra range float |
| pcl::PointWithRange | |
| pcl::_PointWithScale | A point structure representing a 3-D position and scale |
| pcl::PointWithScale | |
| pcl::_PointWithViewpoint | |
| pcl::PointWithViewpoint | A point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen |
| pcl::_PointXYZ | |
| pcl::PointXYZ | A point structure representing Euclidean xyz coordinates |
| pcl::_PointXYZHSV | |
| pcl::PointXYZHSV | |
| pcl::_PointXYZI | A point structure representing Euclidean xyz coordinates, and the intensity value |
| pcl::PointXYZI | |
| pcl::_PointXYZINormal | A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate |
| pcl::PointXYZINormal | |
| pcl::_PointXYZL | |
| pcl::PointXYZL | |
| pcl::_PointXYZRGB | |
| pcl::PointXYZRGB | A point structure representing Euclidean xyz coordinates, and the RGB color |
| pcl::_PointXYZRGBA | A point structure representing Euclidean xyz coordinates, and the RGBA color |
| pcl::PointXYZRGBA | |
| pcl::_PointXYZRGBL | |
| 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::PointXYZRGBNormal | |
| pcl::_ReferenceFrame | A structure representing the Local Reference Frame of a point |
| pcl::ReferenceFrame | |
 pcl::AdaptiveRangeCoder | AdaptiveRangeCoder 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 > | |
| binary_function | |
| 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::BivariatePolynomialT< real > | This represents a bivariate polynomial and provides some functionality for it |
| pcl::BorderDescription | A structure to store if a point in a range image lies on a border between an obstacle and the background |
| pcl::Boundary | A point structure representing a description of whether a point is lying on a surface boundary or not |
| pcl::texture_mapping::Camera | Structure to store camera pose and focal length |
| pcl::visualization::Camera | Camera class holds a set of camera parameters together with the window pos/size |
| pcl::ChannelProperties | ChannelProperties stores the properties of each channel in a cloud, namely: |
| pcl::Clipper3D< PointT > | Base class for 3D clipper objects |
| pcl::PlaneClipper3D< PointT > | Implementation of a plane clipper in 3D |
| cloud_point_index_idx | |
| pcl::visualization::CloudActor | |
| pcl::CloudProperties | CloudProperties stores a list of optional point cloud properties such as: |
| pcl::octree::ColorCoding< PointT > | ColorCoding class |
| pcl::octree::ColorCoding< pcl::PointXYZRGB > | |
| pcl::Comparator< PointT > | Comparator is the base class for comparators that compare two points given some function |
| pcl::EuclideanClusterComparator< PointT, PointNT, PointLT > | EuclideanClusterComparator is a comparator used for finding clusters supported by planar surfaces |
 pcl::PlaneCoefficientComparator< PointT, PointNT > | PlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
 pcl::EdgeAwarePlaneComparator< PointT, PointNT > | EdgeAwarePlaneComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| pcl::EuclideanPlaneCoefficientComparator< PointT, PointNT > | EuclideanPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| pcl::PlaneRefinementComparator< PointT, PointNT, PointLT > | PlaneRefinementComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| pcl::RGBPlaneCoefficientComparator< PointT, PointNT > | RGBPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| pcl::ComparisonBase< PointT > | The (abstract) base class for the comparison object |
| pcl::FieldComparison< PointT > | The field-based specialization of the comparison object |
| pcl::PackedHSIComparison< PointT > | A packed HSI specialization of the comparison object |
| pcl::PackedRGBComparison< PointT > | A packed rgb specialization of the comparison object |
| pcl::TfQuadraticXYZComparison< PointT > | A comparison whether the (x,y,z) components of a given point satisfy (p'Ap + 2v'p + c [OP] 0) |
| pcl::ConditionBase< PointT > | Base condition class |
| pcl::ConditionAnd< PointT > | AND condition |
| pcl::ConditionOr< PointT > | OR condition |
| pcl::octree::configurationProfile_t | |
| ContainerT | |
| pcl::octree::BufferedBranchNode< ContainerT > | |
| pcl::octree::OctreeBranchNode< ContainerT > | Abstract octree branch class |
| pcl::octree::OctreeLeafNode< ContainerT > | Abstract octree leaf class |
| pcl::CopyIfFieldExists< PointInT, OutT > | A helper functor that can copy a specific value if the given field exists |
| pcl::Correspondence | Correspondence represents a match between two entities (e.g., points, descriptors, etc) |
| 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::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::CorrespondenceRejectorMedianDistance | CorrespondenceRejectorMedianDistance implements a simple correspondence rejection method based on thresholding based on the median distance between the correspondences |
| pcl::registration::CorrespondenceRejectorOneToOne | CorrespondenceRejectorOneToOne 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::CorrespondenceRejectorSurfaceNormal | CorrespondenceRejectorSurfaceNormal implements a simple correspondence rejection method based on the angle between the normals at correspondent points |
| pcl::registration::CorrespondenceRejectorTrimmed | CorrespondenceRejectorTrimmed implements a correspondence rejection for ICP-like registration algorithms that uses only the best 'k' correspondences where 'k' is some estimate of the overlap between the two point clouds being registered |
| pcl::registration::CorrespondenceRejectorVarTrimmed | CorrespondenceRejectoVarTrimmed implements a simple correspondence rejection method by considering as inliers a certain percentage of correspondences with the least distances |
| pcl::registration::DataContainerInterface | DataContainerInterface provides a generic interface for computing correspondence scores between correspondent points in the input and target clouds |
| pcl::registration::DataContainer< PointT, NormalT > | DataContainer is a container for the input and target point clouds and implements the interface to compute correspondence scores between correspondent points in the input and target clouds |
| pcl::traits::datatype< PointT, Tag > | |
| pcl::traits::decomposeArray< T > | |
| pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ErrorFunctor | |
| pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::HuberPenalty | |
| pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::TruncatedError | |
| pcl::ESFSignature640 | A point structure representing the Ensemble of Shape Functions (ESF) |
| pcl::FeatureWithLocalReferenceFrames< PointInT, PointRFT > | FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which need a local reference frame at each input keypoint |
| pcl::SHOTEstimationBase< PointInT, PointNT, PointOutT, PointRFT > | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |
| pcl::SHOTEstimation< PointInT, PointNT, PointOutT, PointRFT > | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |
| pcl::SHOTEstimationOMP< PointInT, PointNT, PointOutT, PointRFT > | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |
| pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT > | UniqueShapeContext implements the Unique Shape Descriptor described here: |
| pcl::SHOTEstimationBase< PointInT, PointNT, pcl::SHOT1344, PointRFT > | |
| pcl::SHOTEstimationBase< PointInT, PointNT, pcl::SHOT352, PointRFT > | |
| pcl::SHOTEstimation< PointInT, PointNT, pcl::SHOT352, PointRFT > | |
| pcl::UniqueShapeContext< PointInT, pcl::SHOT, PointRFT > | |
| pcl::UniqueShapeContext< PointInT, Eigen::MatrixXf, PointRFT > | UniqueShapeContext implements the Unique Shape Descriptor described here: |
| pcl::detail::FieldAdder< PointT > | |
| pcl::traits::fieldList< PointT > | |
| pcl::detail::FieldMapper< PointT > | |
| pcl::detail::FieldMapping | |
| pcl::FieldMatches< PointT, Tag > | |
| pcl::FileReader | Point Cloud Data (FILE) file format reader interface |
| pcl::PCDReader | Point Cloud Data (PCD) file format reader |
| pcl::PLYReader | Point Cloud Data (PLY) file format reader |
| pcl::FileWriter | Point Cloud Data (FILE) file format writer |
| pcl::PCDWriter | Point Cloud Data (PCD) file format writer |
| pcl::PLYWriter | Point Cloud Data (PLY) file format writer |
| pcl::search::FlannSearch< PointT, FlannDistance >::FlannIndexCreator | Helper class that creates a FLANN index from a given FLANN matrix |
| pcl::search::FlannSearch< PointT, FlannDistance >::KdTreeIndexCreator | Creates a FLANN KdTreeSingleIndex from the given input data |
| pcl::visualization::FloatImageUtils | Provide some gerneral functionalities regarding 2d float arrays, e.g., for visualization purposes |
| pcl::for_each_type_impl< done > | |
| pcl::for_each_type_impl< false > | |
| pcl::FPFHSignature33 | A point structure representing the Fast Point Feature Histogram (FPFH) |
| pcl::Functor< _Scalar, NX, NY > | 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::Functor< float > | |
| pcl::GaussianKernel | Class GaussianKernel assembles all the method for computing, convolving, smoothing, gradients computing an image using a gaussian kernel |
| pcl::GFPFHSignature16 | A point structure representing the GFPFH descriptor with 16 bins |
| pcl::Grabber | Grabber interface for PCL 1.x device drivers |
| pcl::PCDGrabberBase | Base class for PCD file grabber |
| pcl::PCDGrabber< PointT > | |
| std_msgs::Header | |
| pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT, NrDims > | |
| pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT[NrDims], NrDims > | |
| pcl::Histogram< N > | A point structure representing an N-D histogram |
| sensor_msgs::Image | |
| pcl::visualization::ImageViewer | ImageViewer is a class for 2D image visualization |
| pcl::visualization::RangeImageVisualizer | Range image visualizer class |
| pcl::IntegralImage2D< DataType, Dimension > | Determines an integral image representation for a given organized data array |
| pcl::IntegralImage2D< DataType, 1 > | Partial template specialization for integral images with just one channel |
| pcl::IntegralImage2D< float, 1 > | |
| pcl::IntegralImage2D< float, 3 > | |
| pcl::IntegralImageTypeTraits< DataType > | |
| pcl::IntegralImageTypeTraits< char > | |
| pcl::IntegralImageTypeTraits< float > | |
| pcl::IntegralImageTypeTraits< int > | |
| pcl::IntegralImageTypeTraits< short > | |
| pcl::IntegralImageTypeTraits< unsigned char > | |
| pcl::IntegralImageTypeTraits< unsigned int > | |
| pcl::IntegralImageTypeTraits< unsigned short > | |
| pcl::common::IntensityFieldAccessor< PointT > | |
| pcl::common::IntensityFieldAccessor< pcl::PointNormal > | |
| pcl::common::IntensityFieldAccessor< pcl::PointXYZRGB > | |
| pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBA > | |
| pcl::common::IntensityFieldAccessor< PointInT > | |
| pcl::IntensityGradient | A point structure representing the intensity gradient of an XYZI point cloud |
| pcl::InterestPoint | A point structure representing an interest point with Euclidean xyz coordinates, and an interest value |
| pcl::intersect< Sequence1, Sequence2 > | |
| openni_wrapper::IRImage | Class containing just a reference to IR meta data |
| pcl::PosesFromMatches::PoseEstimate::IsBetter | |
| iterator | |
| pcl::octree::OctreeIteratorBase< DataT, OctreeT > | Abstract octree iterator class |
| pcl::octree::OctreeBreadthFirstIterator< DataT, OctreeT > | Octree iterator class |
| pcl::octree::OctreeDepthFirstIterator< DataT, OctreeT > | Octree iterator class |
| pcl::octree::OctreeLeafNodeIterator< DataT, OctreeT > | Octree leaf node iterator class |
| pcl::KdTree< PointT > | KdTree represents the base spatial locator class for kd-tree implementations |
| pcl::KdTreeFLANN< PointT, Dist > | KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures |
| pcl::KdTree< FeatureT > | |
| pcl::KdTreeFLANN< FeatureT > | |
| pcl::KdTree< pcl::VFHSignature308 > | |
| pcl::KdTreeFLANN< pcl::VFHSignature308 > | |
| pcl::KdTree< PointSource > | |
| pcl::KdTree< PointTarget > | |
| pcl::KdTreeFLANN< PointTarget > | |
| pcl::KdTreeFLANN< Eigen::MatrixXf > | KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures |
| pcl::visualization::KeyboardEvent | /brief Class representing key hit/release events |
| flann::L2< T > | |
| flann::L2_Simple< T > | |
| pcl::Label | |
| pcl::GridProjection< PointNT >::Leaf | Data leaf |
| pcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGrid::Leaf | |
| pcl::io::ply::ply_parser::list_property_begin_callback_type< SizeType, ScalarType > | |
| pcl::io::ply::ply_parser::list_property_begin_callback_type< size_type, scalar_type > | |
| pcl::io::ply::ply_parser::list_property_definition_callback_type< SizeType, ScalarType > | |
| pcl::io::ply::ply_parser::list_property_definition_callback_type< size_type, scalar_type > | |
| pcl::io::ply::ply_parser::list_property_definition_callbacks_type | |
| pcl::io::ply::ply_parser::list_property_element_callback_type< SizeType, ScalarType > | |
| pcl::io::ply::ply_parser::list_property_element_callback_type< size_type, scalar_type > | |
| pcl::io::ply::ply_parser::list_property_end_callback_type< SizeType, ScalarType > | |
| pcl::io::ply::ply_parser::list_property_end_callback_type< size_type, scalar_type > | |
| pcl::RangeImageBorderExtractor::LocalSurface | Stores some information extracted from the neighborhood of a point |
| flann::Matrix< T > | |
| Mesh | |
| pcl::MeshProcessing | MeshProcessing represents the base class for mesh processing algorithms |
| pcl::EarClipping | The ear clipping triangulation algorithm |
| pcl::MeshSmoothingLaplacianVTK | PCL mesh smoothing based on the vtkSmoothPolyDataFilter algorithm from the VTK library |
| pcl::MeshSmoothingWindowedSincVTK | PCL mesh smoothing based on the vtkWindowedSincPolyDataFilter algorithm from the VTK library |
| pcl::MeshSubdivisionVTK | PCL mesh smoothing based on the vtkLinearSubdivisionFilter, vtkLoopSubdivisionFilter, vtkButterflySubdivisionFilter depending on the selected MeshSubdivisionVTKFilterType algorithm from the VTK library |
| pcl::ModelCoefficients | |
| pcl::MomentInvariants | A point structure representing the three moment invariants |
| pcl::visualization::MouseEvent | |
| pcl::traits::name< PointT, Tag, dummy > | |
| 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::NdCentroidFunctor< PointT > | Helper functor structure for n-D centroid estimation |
| pcl::NdConcatenateFunctor< PointInT, PointOutT > | Helper functor structure for concatenate |
| pcl::NdCopyEigenPointFunctor< PointOutT > | Helper functor structure for copying data between an Eigen type and a PointT |
| pcl::NdCopyPointEigenFunctor< PointInT > | Helper functor structure for copying data between an Eigen type and a PointT |
| flann::NNIndex< T > | |
| noncopyable | |
| pcl::visualization::CloudViewer | Simple point cloud visualization class |
| pcl::NormalBasedSignature12 | A point structure representing the Normal Based Signature for a feature matrix of 4-by-3 |
| ObjectFeatures | |
| ObjectModel | |
| ObjectRecognition | |
| ObjectRecognitionParameters | |
| pcl::octree::Octree2BufBase< DataT, LeafT, BranchT > | Octree double buffer class |
| pcl::octree::Octree2BufBase< int, LeafT, BranchT > | |
| pcl::octree::OctreePointCloud< PointT, LeafT, BranchT, Octree2BufBase< int, LeafT, BranchT > > | |
| pcl::octree::OctreePointCloudChangeDetector< PointT, LeafT, BranchT > | Octree pointcloud change detector class |
| pcl::octree::OctreeBase< DataT, LeafT, BranchT > | Octree class |
| pcl::octree::OctreeBase< int, LeafT, BranchT > | |
| pcl::octree::OctreePointCloud< PointT, LeafT, BranchT > | |
| pcl::octree::OctreePointCloudDensity< PointT, LeafT, BranchT > | Octree pointcloud density class |
| pcl::octree::OctreePointCloudSearch< PointT, LeafT, BranchT > | Octree pointcloud search class |
| pcl::octree::OctreePointCloudVoxelCentroid< PointT, LeafT, BranchT > | Octree pointcloud voxel centroid class |
| pcl::octree::OctreePointCloud< PointT, LeafT, BranchT, OctreeBase< int, LeafT, BranchT > > | |
| pcl::octree::OctreePointCloudOccupancy< PointT, LeafT, BranchT > | Octree pointcloud occupancy class |
| pcl::octree::OctreeContainerDataT< DataT > | Octree leaf class that does store a single DataT element |
| pcl::octree::OctreeContainerDataTVector< DataT > | Octree leaf class that does store a vector of DataT elements |
| pcl::octree::OctreeContainerEmpty< DataT > | Octree leaf class that does not store any information |
| pcl::octree::OctreeKey | Octree key class |
| pcl::octree::OctreeNode | Abstract octree node class |
| pcl::octree::BufferedBranchNode< ContainerT > | |
| pcl::octree::OctreeBranchNode< ContainerT > | Abstract octree branch class |
| pcl::octree::OctreeLeafNode< ContainerT > | Abstract octree leaf class |
| pcl::octree::OctreeNodePool< NodeT > | Octree node pool |
| pcl::octree::OctreeNodePool< pcl::octree::BufferedBranchNode > | |
| pcl::octree::OctreeNodePool< pcl::octree::OctreeBranchNode > | |
| pcl::octree::OctreeNodePool< pcl::octree::OctreeLeafNode > | |
| pcl::octree::OctreePointCloudDensityContainer< DataT > | Octree pointcloud density leaf node class |
| pcl::traits::offset< PointT, Tag > | |
| OpenNICapture | |
| pcl::OrganizedIndexIterator | Base class for iterators on 2-dimensional maps like images/organized clouds etc |
| pcl::LineIterator | Organized Index Iterator for iterating over the "pixels" for a given line using the Bresenham algorithm |
| pair | |
| pcl::PPFHashMapSearch::HashKeyStruct | Data structure to hold the information for the key in the feature hash map of the PPFHashMapSearch class |
| pcl::PosesFromMatches::Parameters | Parameters used in this class |
| pcl::RangeImageBorderExtractor::Parameters | Parameters used in this class |
| pcl::NarfKeypoint::Parameters | Parameters used in this class |
| pcl::PolynomialCalculationsT< real >::Parameters | Parameters used in this class |
| pcl::NarfDescriptor::Parameters | |
| pcl::PCLBase< PointT > | PCL base class |
| pcl::Feature< PointT, PointFeature > | |
| pcl::FeatureFromNormals< PointT, PointNT, PointFeature > | |
| pcl::NormalBasedSignatureEstimation< PointT, PointNT, PointFeature > | Normal-based feature signature estimation class |
| pcl::Keypoint< PointT, PointT > | |
| pcl::SmoothedSurfacesKeypoint< PointT, PointNT > | Based on the paper: Xinju Li and Igor Guskov Multi-scale features for approximate alignment of point-based surfaces Proceedings of the third Eurographics symposium on Geometry processing July 2005, Vienna, Austria |
| pcl::EuclideanClusterExtraction< PointT > | EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense |
| 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::Filter< PointT > | Filter represents the base filter class |
| pcl::ApproximateVoxelGrid< PointT > | ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| pcl::BilateralFilter< PointT > | A bilateral filter implementation for point cloud data |
| pcl::ConditionalRemoval< PointT > | ConditionalRemoval filters data that satisfies certain conditions |
| pcl::FilterIndices< PointT > | FilterIndices represents the base class for filters that are about binary point removal |
| pcl::CropBox< PointT > | CropBox is a filter that allows the user to filter all the data inside of a given box |
| pcl::CropHull< PointT > | Filter points that lie inside or outside a 3D closed surface or 2D closed polygon, as generated by the ConvexHull or ConcaveHull classes |
| pcl::ExtractIndices< PointT > | ExtractIndices extracts a set of indices from a point cloud |
| pcl::NormalSpaceSampling< PointT, NormalT > | NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every point |
| pcl::PassThrough< PointT > | PassThrough passes points in a cloud based on constraints for one particular field of the point type |
| pcl::RadiusOutlierRemoval< PointT > | RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have |
| pcl::RandomSample< PointT > | RandomSample applies a random sampling with uniform probability |
| pcl::StatisticalOutlierRemoval< PointT > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |
| pcl::ProjectInliers< PointT > | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |
| pcl::VoxelGrid< PointT > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| pcl::LabeledEuclideanClusterExtraction< PointT > | LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info |
| pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT > | OrganizedConnectedComponentSegmentation allows connected components to be found within organized point cloud data, given a comparison function |
| pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT > | OrganizedMultiPlaneSegmentation finds all planes present in the input cloud, and outputs a vector of plane equations, as well as a vector of point clouds corresponding to the inliers of each detected plane |
| pcl::PCA< PointT > | Principal Component analysis (PCA) class |
| pcl::registration::ELCH< PointT > | ELCH (Explicit Loop Closing Heuristic) class |
| 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::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::StatisticalMultiscaleInterestRegionExtraction< PointT > | Class for extracting interest regions from unstructured point clouds, based on a multi scale statistical approach |
| pcl::SurfelSmoothing< PointT, PointNT > | |
| pcl::PCLBase< PointFeature > | |
| pcl::PyramidFeatureHistogram< PointFeature > | Class that compares two sets of features by using a multiscale representation of the features inside a pyramid |
| pcl::PCLBase< PointInT > | |
| pcl::Feature< PointInT, pcl::Boundary > | |
| pcl::FeatureFromNormals< PointInT, PointNT, pcl::Boundary > | |
| pcl::BoundaryEstimation< PointInT, PointNT, pcl::Boundary > | |
| pcl::BoundaryEstimation< PointInT, PointNT, Eigen::MatrixXf > | BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion |
| pcl::Feature< PointInT, pcl::FPFHSignature33 > | |
| pcl::FeatureFromNormals< PointInT, PointNT, pcl::FPFHSignature33 > | |
| pcl::FPFHEstimation< PointInT, PointNT, pcl::FPFHSignature33 > | |
| pcl::FPFHEstimation< PointInT, PointNT, Eigen::MatrixXf > | FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals |
| pcl::Feature< PointInT, pcl::Histogram< 153 > > | |
| pcl::SpinImageEstimation< PointInT, PointNT, pcl::Histogram< 153 > > | |
| pcl::SpinImageEstimation< PointInT, PointNT, Eigen::MatrixXf > | Estimates spin-image descriptors in the given input points |
| pcl::Feature< PointInT, pcl::Histogram< 20 > > | |
| pcl::IntensitySpinEstimation< PointInT, pcl::Histogram< 20 > > | |
| pcl::IntensitySpinEstimation< PointInT, Eigen::MatrixXf > | IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity |
| pcl::Feature< PointInT, pcl::Histogram< 32 > > | |
| pcl::RIFTEstimation< PointInT, GradientT, pcl::Histogram< 32 > > | |
| pcl::RIFTEstimation< PointInT, GradientT, Eigen::MatrixXf > | RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity |
| pcl::Feature< PointInT, pcl::IntensityGradient > | |
| pcl::FeatureFromNormals< PointInT, PointNT, pcl::IntensityGradient > | |
| pcl::IntensityGradientEstimation< PointInT, PointNT, pcl::IntensityGradient > | |
| pcl::IntensityGradientEstimation< PointInT, PointNT, Eigen::MatrixXf > | IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values |
| pcl::Feature< PointInT, pcl::MomentInvariants > | |
| pcl::MomentInvariantsEstimation< PointInT, pcl::MomentInvariants > | |
| pcl::MomentInvariantsEstimation< PointInT, Eigen::MatrixXf > | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |
| pcl::Feature< PointInT, pcl::Normal > | |
| pcl::NormalEstimation< PointInT, pcl::Normal > | |
| pcl::NormalEstimationOMP< PointInT, pcl::Normal > | |
| pcl::NormalEstimationOMP< PointInT, Eigen::MatrixXf > | NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard |
| pcl::NormalEstimation< PointInT, Eigen::MatrixXf > | NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures |
| pcl::Feature< PointInT, pcl::PFHSignature125 > | |
| pcl::FeatureFromNormals< PointInT, PointNT, pcl::PFHSignature125 > | |
| pcl::PFHEstimation< PointInT, PointNT, pcl::PFHSignature125 > | |
| pcl::PFHEstimation< PointInT, PointNT, Eigen::MatrixXf > | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |
| pcl::Feature< PointInT, pcl::PPFSignature > | |
| pcl::FeatureFromNormals< PointInT, PointNT, pcl::PPFSignature > | |
| pcl::PPFEstimation< PointInT, PointNT, pcl::PPFSignature > | |
| pcl::PPFEstimation< PointInT, PointNT, Eigen::MatrixXf > | Class that calculates the "surflet" features for each pair in the given pointcloud |
| pcl::Feature< PointInT, pcl::PrincipalCurvatures > | |
| pcl::FeatureFromNormals< PointInT, PointNT, pcl::PrincipalCurvatures > | |
| pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, pcl::PrincipalCurvatures > | |
| pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, Eigen::MatrixXf > | PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals |
| pcl::Feature< PointInT, pcl::SHOT > | |
| pcl::FeatureFromNormals< PointInT, PointNT, pcl::SHOT > | |
| pcl::ShapeContext3DEstimation< PointInT, PointNT, pcl::SHOT > | |
| pcl::ShapeContext3DEstimation< PointInT, PointNT, Eigen::MatrixXf > | ShapeContext3DEstimation implements the 3D shape context descriptor as described in: |
| pcl::UniqueShapeContext< PointInT, pcl::SHOT, PointRFT > | |
| pcl::Feature< PointInT, pcl::SHOT1344 > | |
| pcl::FeatureFromNormals< PointInT, PointNT, pcl::SHOT1344 > | |
| pcl::SHOTEstimationBase< PointInT, PointNT, pcl::SHOT1344, PointRFT > | |
| pcl::Feature< PointInT, pcl::SHOT352 > | |
| pcl::FeatureFromNormals< PointInT, PointNT, pcl::SHOT352 > | |
| pcl::SHOTEstimationBase< PointInT, PointNT, pcl::SHOT352, PointRFT > | |
| pcl::Keypoint< PointInT, int > | |
| pcl::UniformSampling< PointInT > | UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| pcl::CloudSurfaceProcessing< PointInT, PointOutT > | CloudSurfaceProcessing represents the base class for algorithms that take a point cloud as an input and produce a new output cloud that has been modified towards a better surface representation |
| pcl::BilateralUpsampling< PointInT, PointOutT > | Bilateral filtering implementation, based on the following paper: |
| pcl::MovingLeastSquares< PointInT, PointOutT > | MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation |
| pcl::MovingLeastSquaresOMP< PointInT, PointOutT > | MovingLeastSquaresOMP represent an OpenMP implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation |
| pcl::Feature< PointInT, PointOutT > | Feature represents the base feature class |
| pcl::ESFEstimation< PointInT, PointOutT > | ESFEstimation estimates the ensemble of shape functions descriptors for a given point cloud dataset containing points |
| pcl::FeatureFromLabels< PointInT, PointLT, PointOutT > | |
| pcl::FeatureFromNormals< PointInT, PointNT, PointOutT > | |
| pcl::BoundaryEstimation< PointInT, PointNT, PointOutT > | BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion |
| pcl::CVFHEstimation< PointInT, PointNT, PointOutT > | CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in: |
| pcl::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::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT > | IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values |
| pcl::PFHEstimation< PointInT, PointNT, PointOutT > | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |
| pcl::PFHRGBEstimation< PointInT, PointNT, PointOutT > | |
| pcl::PPFEstimation< PointInT, PointNT, PointOutT > | Class that calculates the "surflet" features for each pair in the given pointcloud |
| pcl::PPFRGBEstimation< PointInT, PointNT, PointOutT > | |
| pcl::PPFRGBRegionEstimation< PointInT, PointNT, PointOutT > | |
| 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::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::ShapeContext3DEstimation< PointInT, PointNT, PointOutT > | ShapeContext3DEstimation implements the 3D shape context descriptor as described in: |
| pcl::SHOTEstimationBase< PointInT, PointNT, PointOutT, PointRFT > | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |
| pcl::VFHEstimation< PointInT, PointNT, PointOutT > | VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals |
| pcl::IntegralImageNormalEstimation< PointInT, PointOutT > | Surface normal estimation on organized data using integral images |
| pcl::IntensitySpinEstimation< PointInT, PointOutT > | IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity |
| pcl::MomentInvariantsEstimation< PointInT, PointOutT > | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |
| pcl::NormalEstimation< PointInT, PointOutT > | NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point |
| 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::RIFTEstimation< PointInT, GradientT, PointOutT > | RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity |
| pcl::SHOTLocalReferenceFrameEstimation< PointInT, PointOutT > | SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor |
| pcl::SHOTLocalReferenceFrameEstimationOMP< PointInT, PointOutT > | SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor |
| pcl::SpinImageEstimation< PointInT, PointNT, PointOutT > | Estimates spin-image descriptors in the given input points |
| pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT > | UniqueShapeContext implements the Unique Shape Descriptor described here: |
| pcl::Keypoint< PointInT, PointOutT > | Keypoint represents the base class for key points |
| pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT > | HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals |
| pcl::SIFTKeypoint< PointInT, PointOutT > | SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity |
| pcl::PCLSurfaceBase< PointInT > | Pure abstract class |
| pcl::MeshConstruction< PointInT > | MeshConstruction represents a base surface reconstruction class |
| pcl::GreedyProjectionTriangulation< PointInT > | GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections |
| pcl::OrganizedFastMesh< PointInT > | Simple triangulation/surface reconstruction for organized point clouds |
| pcl::SurfaceReconstruction< PointInT > | SurfaceReconstruction represents a base surface reconstruction class |
| pcl::PCLBase< PointNT > | |
| pcl::PCLSurfaceBase< PointNT > | |
| pcl::SurfaceReconstruction< PointNT > | |
| pcl::GridProjection< PointNT > | Grid projection surface reconstruction method |
| pcl::MarchingCubes< PointNT > | The marching cubes surface reconstruction algorithm |
| pcl::MarchingCubesHoppe< PointNT > | The marching cubes surface reconstruction algorithm, using a signed distance function based on the distance from tangent planes, proposed by Hoppe et |
| pcl::MarchingCubesRBF< PointNT > | The marching cubes surface reconstruction algorithm, using a signed distance function based on radial basis functions |
| pcl::Poisson< PointNT > | The Poisson surface reconstruction algorithm |
| pcl::PCLBase< PointSource > | |
| pcl::Feature< PointSource, PointFeature > | |
| 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::Registration< PointSource, PointTarget > | Registration represents the base registration class |
| pcl::IterativeClosestPoint< PointSource, PointTarget > | IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm |
| 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::IterativeClosestPointNonLinear< PointSource, PointTarget > | IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend |
| pcl::PPFRegistration< PointSource, PointTarget > | Class that registers two point clouds based on their sets of PPFSignatures |
| 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::registration::CorrespondenceEstimation< PointSource, PointTarget > | CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features |
| pcl::registration::CorrespondenceEstimationNormalShooting< PointSource, PointTarget, NormalT > | CorrespondenceEstimationNormalShooting computes correspondences as points in the target cloud which have minimum distance to normals computed on the input cloud |
| pcl::PCLBase< PointWithRange > | |
| pcl::Feature< PointWithRange, BorderDescription > | |
| pcl::RangeImageBorderExtractor | Extract obstacle borders from range images, meaning positions where there is a transition from foreground to background |
| pcl::Feature< PointWithRange, Narf36 > | |
| pcl::NarfDescriptor | Computes NARF feature descriptors for points in a range image |
| pcl::Keypoint< PointWithRange, int > | |
| pcl::NarfKeypoint | NARF (Normal Aligned Radial Feature) keypoints |
| pcl::PCLBase< sensor_msgs::PointCloud2 > | |
| pcl::Filter< sensor_msgs::PointCloud2 > | Filter represents the base filter class |
| pcl::FilterIndices< sensor_msgs::PointCloud2 > | FilterIndices represents the base class for filters that are about binary point removal |
| pcl::CropBox< sensor_msgs::PointCloud2 > | CropBox is a filter that allows the user to filter all the data inside of a given box |
| pcl::ExtractIndices< sensor_msgs::PointCloud2 > | ExtractIndices extracts a set of indices from a point cloud |
| pcl::RandomSample< sensor_msgs::PointCloud2 > | RandomSample applies a random sampling with uniform probability |
| pcl::PassThrough< sensor_msgs::PointCloud2 > | PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints |
| 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::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::StatisticalOutlierRemoval< sensor_msgs::PointCloud2 > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |
| pcl::VoxelGrid< sensor_msgs::PointCloud2 > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| pcl::visualization::PCLHistogramVisualizer | PCL histogram visualizer main class |
| pcl::visualization::PCLVisualizer | PCL Visualizer main class |
| pcl::PFHRGBSignature250 | A point structure representing the Point Feature Histogram with colors (PFHRGB) |
| pcl::PFHSignature125 | A point structure representing the Point Feature Histogram (PFH) |
| pcl::PiecewiseLinearFunction | This provides functionalities to efficiently return values for piecewise linear function |
| pcl::PlanarPolygon< PointT > | PlanarPolygon represents a planar (2D) polygon, potentially in a 3D space |
| pcl::PlanarRegion< PointT > | PlanarRegion represents a set of points that lie in a plane |
| pcl::PlanarPolygonFusion< PointT > | PlanarPolygonFusion takes a list of 2D planar polygons and attempts to reduce them to a minimum set that best represents the scene, based on various given comparators |
| pcl::io::ply::ply_parser | Class ply_parser parses a PLY file and generates appropriate atomic parsers for the body |
| pcl::traits::POD< PointT > | |
| pcl::PointCloud< PointT > | PointCloud represents the base class in PCL for storing collections of 3D points |
| sensor_msgs::PointCloud2 | |
| pcl::PointCloud< Eigen::MatrixXf > | PointCloud specialization for Eigen matrices |
| pcl::PointCloud< FeatureT > | |
| pcl::PointCloud< GlobalDescriptorT > | |
| pcl::PointCloud< LocalDescriptorT > | |
| pcl::PointCloud< NormalT > | |
| pcl::PointCloud< pcl::InterestPoint > | |
| pcl::PointCloud< pcl::Normal > | |
| pcl::PointCloud< pcl::pcl::PointXYZ > | |
| pcl::PointCloud< pcl::PointXYZRGB > | |
| pcl::PointCloud< PointInT > | |
| pcl::PointCloud< PointNT > | |
| pcl::PointCloud< PointSource > | |
| pcl::PointCloud< PointTarget > | |
| pcl::PointCloud< PointWithRange > | |
| 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::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::PointCloud< PointXYZ > | |
| pcl::PointCloud< T > | |
| pcl::visualization::PointCloudColorHandler< PointT > | Base Handler class for PointCloud colors |
| pcl::visualization::PointCloudColorHandlerCustom< PointT > | Handler for predefined user colors |
| pcl::visualization::PointCloudColorHandlerGenericField< PointT > | Generic field handler class for colors |
| pcl::visualization::PointCloudColorHandlerHSVField< PointT > | HSV handler class for colors |
| pcl::visualization::PointCloudColorHandlerRandom< PointT > | 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::PointCloudColorHandler< sensor_msgs::PointCloud2 > | Base Handler class for PointCloud colors |
| pcl::visualization::PointCloudColorHandlerCustom< sensor_msgs::PointCloud2 > | Handler for predefined user colors |
| pcl::visualization::PointCloudColorHandlerGenericField< sensor_msgs::PointCloud2 > | Generic field handler class for colors |
| pcl::visualization::PointCloudColorHandlerHSVField< sensor_msgs::PointCloud2 > | HSV handler class for colors |
| pcl::visualization::PointCloudColorHandlerRandom< sensor_msgs::PointCloud2 > | Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen) |
| pcl::visualization::PointCloudColorHandlerRGBField< sensor_msgs::PointCloud2 > | RGB handler class for colors |
| pcl::visualization::PointCloudGeometryHandler< PointT > | Base handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerCustom< PointT > | Custom handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< PointT > | Surface normal handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerXYZ< PointT > | XYZ handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandler< sensor_msgs::PointCloud2 > | Base handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerCustom< sensor_msgs::PointCloud2 > | Custom handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< sensor_msgs::PointCloud2 > | Surface normal handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerXYZ< sensor_msgs::PointCloud2 > | XYZ handler class for PointCloud geometry |
| pcl::octree::PointCoding< PointT > | PointCoding class |
| pcl::octree::PointCoding< pcl::PointXYZRGB > | |
| pcl::PointDataAtOffset< PointT > | A datatype that enables type-correct comparisons |
| pcl::PointDataAtOffset< pcl::PointXYZRGB > | |
| sensor_msgs::PointField | |
| pcl::PointIndices | |
| 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::PointRepresentation< FeatureT > | |
| pcl::PointRepresentation< FPFHSignature33 > | |
| pcl::DefaultFeatureRepresentation< FPFHSignature33 > | |
| pcl::DefaultPointRepresentation< FPFHSignature33 > | |
| pcl::PointRepresentation< Narf * > | |
| pcl::Narf::FeaturePointRepresentation | |
| pcl::PointRepresentation< NormalBasedSignature12 > | |
| pcl::DefaultFeatureRepresentation< NormalBasedSignature12 > | |
| pcl::DefaultPointRepresentation< NormalBasedSignature12 > | |
| pcl::PointRepresentation< PFHRGBSignature250 > | |
| pcl::DefaultFeatureRepresentation< PFHRGBSignature250 > | |
| pcl::DefaultPointRepresentation< PFHRGBSignature250 > | |
| pcl::PointRepresentation< PFHSignature125 > | |
| pcl::DefaultFeatureRepresentation< PFHSignature125 > | |
| pcl::DefaultPointRepresentation< PFHSignature125 > | |
| pcl::PointRepresentation< PointDefault > | |
| pcl::CustomPointRepresentation< PointDefault > | CustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point |
| pcl::DefaultFeatureRepresentation< PointDefault > | DefaulFeatureRepresentation extends PointRepresentation and is intended to be used when defining the default behavior for feature descriptor types (i.e., copy each element of each field into a float array) |
| pcl::DefaultPointRepresentation< PointDefault > | DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types |
| pcl::PointRepresentation< PointNormal > | |
| pcl::DefaultPointRepresentation< PointNormal > | |
| pcl::PointRepresentation< PointXYZ > | |
| pcl::DefaultPointRepresentation< PointXYZ > | |
| pcl::PointRepresentation< PointXYZI > | |
| pcl::DefaultPointRepresentation< PointXYZI > | |
| pcl::PointRepresentation< PPFSignature > | |
| pcl::DefaultFeatureRepresentation< PPFSignature > | |
| pcl::DefaultPointRepresentation< PPFSignature > | |
| pcl::PointRepresentation< ShapeContext > | |
| pcl::DefaultPointRepresentation< ShapeContext > | |
| pcl::PointRepresentation< SHOT1344 > | |
| pcl::DefaultPointRepresentation< SHOT1344 > | |
| pcl::PointRepresentation< SHOT352 > | |
| pcl::DefaultPointRepresentation< SHOT352 > | |
| pcl::PointRepresentation< VFHSignature308 > | |
| pcl::DefaultFeatureRepresentation< VFHSignature308 > | |
| pcl::DefaultPointRepresentation< VFHSignature308 > | |
| pcl::PointXY | A 2D point structure representing Euclidean xy coordinates |
| pcl::PolygonMesh | |
| pcl::PolynomialCalculationsT< real > | This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials |
| pcl::PosesFromMatches::PoseEstimate | A result of the pose estimation process |
| pcl::PosesFromMatches | Calculate 3D transformation based on point correspondencdes |
| pcl::PPFRegistration< PointSource, PointTarget >::PoseWithVotes | Structure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes |
| pcl::PPFHashMapSearch | |
| 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::PrincipalRadiiRSD | A point structure representing the minimum and maximum surface radii (in meters) computed using RSD |
| pcl::Region3D< PointT > | Region3D represents summary statistics of a 3D collection of points |
| pcl::PlanarRegion< PointT > | PlanarRegion represents a set of points that lie in a plane |
| 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::RGB | A structure representing RGB color information |
| pcl::TexMaterial::RGB | |
| runtime_error | |
| pcl::PCLException | A base class for all pcl exceptions which inherits from std::runtime_error |
| pcl::ComputeFailedException | |
| pcl::InitFailedException | An exception thrown when init can not be performed should be used in all the PCLBase class inheritants |
| 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) |
| pcl::IsNotDenseException | An exception that is thrown when a PointCloud is not dense but is attemped to be used as dense |
| pcl::KernelWidthTooSmallException | An exception that is thrown when the kernel size is too small |
| pcl::NotEnoughPointsException | An exception that is thrown when the number of correspondants is not equal to the minimum required |
| pcl::PCLIOException | Base exception class for I/O operations |
| pcl::SolverDidntConvergeException | An exception that is thrown when the non linear solver didn't converge |
| pcl::UnhandledPointTypeException | |
| pcl::UnorganizedPointCloudException | An exception that is thrown when an organized point cloud is needed but not provided |
| pcl::SampleConsensus< T > | SampleConsensus represents the base class |
| pcl::SampleConsensus< PointT > | |
| pcl::LeastMedianSquares< PointT > | LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) 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::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::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::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::SampleConsensusModelCone< PointT, PointNT > | SampleConsensusModelCone defines a model for 3D cone segmentation |
| pcl::SampleConsensusModelCylinder< PointT, PointNT > | SampleConsensusModelCylinder defines a model for 3D cylinder segmentation |
| pcl::SampleConsensusModelLine< PointT > | SampleConsensusModelLine defines a model for 3D line segmentation |
| pcl::SampleConsensusModelParallelLine< PointT > | SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints |
| pcl::SampleConsensusModelPlane< PointT > | SampleConsensusModelPlane defines a model for 3D plane 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::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::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::SampleConsensusModelNormalSphere< PointT, PointNT > | SampleConsensusModelNormalSphere defines a model for 3D sphere segmentation using additional surface normal constraints |
| pcl::SampleConsensusModelStick< PointT > | SampleConsensusModelStick defines a model for 3D stick segmentation |
| pcl::SampleConsensusModel< pcl::PointXYZRGB > | |
| pcl::SampleConsensusModelCircle2D< pcl::PointXYZRGB > | |
| pcl::SampleConsensusModelCone< pcl::PointXYZRGB, PointNT > | |
| pcl::SampleConsensusModelCylinder< pcl::PointXYZRGB, PointNT > | |
| pcl::SampleConsensusModelSphere< pcl::PointXYZRGB > | |
| pcl::SampleConsensusModel< PointXYZ > | |
| pcl::SampleConsensusModel< T > | |
| pcl::SampleConsensusModelFromNormals< PointT, PointNT > | SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation |
| pcl::SampleConsensusModelCone< PointT, PointNT > | SampleConsensusModelCone defines a model for 3D cone segmentation |
| pcl::SampleConsensusModelCylinder< PointT, PointNT > | SampleConsensusModelCylinder defines a model for 3D cylinder 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::SampleConsensusModelNormalSphere< PointT, PointNT > | SampleConsensusModelNormalSphere defines a model for 3D sphere segmentation using additional surface normal constraints |
| pcl::SampleConsensusModelFromNormals< pcl::PointXYZRGB, PointNT > | |
| pcl::SampleConsensusModelCone< pcl::PointXYZRGB, PointNT > | |
| pcl::SampleConsensusModelCylinder< pcl::PointXYZRGB, PointNT > | |
| pcl::io::ply::ply_parser::scalar_property_callback_type< ScalarType > | |
| pcl::io::ply::ply_parser::scalar_property_callback_type< scalar_type > | |
| pcl::io::ply::ply_parser::scalar_property_definition_callback_type< ScalarType > | |
| pcl::io::ply::ply_parser::scalar_property_definition_callback_type< scalar_type > | |
| pcl::io::ply::ply_parser::scalar_property_definition_callbacks_type | |
| pcl::search::Search< PointT > | Generic search class |
| pcl::search::BruteForce< PointT > | Implementation of a simple brute force search algorithm |
| pcl::search::FlannSearch< PointT, FlannDistance > | search::FlannSearch is a generic FLANN wrapper class for the new search interface |
| pcl::search::KdTree< PointT > | search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure |
| pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT > | search::Octree is a wrapper class which implements nearest neighbor search operations based on the pcl::octree::Octree structure |
| pcl::search::OrganizedNeighbor< PointT > | OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds |
| pcl::search::Search< pcl::PointXYZ > | |
| pcl::search::Search< pcl::PointXYZRGB > | |
| pcl::search::Search< PointInT > | |
| pcl::search::Search< PointNT > | |
| pcl::search::Search< PointSource > | |
| pcl::search::Search< PointWithRange > | |
| pcl::search::Search< PointXYZ > | |
| pcl::search::Search< T > | |
| pcl::SetIfFieldExists< PointOutT, InT > | A helper functor that can set a specific value in a field if the field exists |
| pcl::RangeImageBorderExtractor::ShadowBorderIndices | Stores the indices of the shadow border corresponding to obstacle borders |
| pcl::ShapeContext | A point structure representing a Shape Context |
| pcl::SHOT | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) |
| pcl::SHOT1344 | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape+color |
| pcl::SHOT352 | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape only |
| pcl::SHOTColorEstimationOMP< PointInT, PointNT, PointOutT, PointRFT > | |
| pcl::SIFTKeypointFieldSelector< PointT > | |
| pcl::SIFTKeypointFieldSelector< PointInT > | |
| pcl::SIFTKeypointFieldSelector< PointNormal > | |
| pcl::SIFTKeypointFieldSelector< PointXYZRGB > | |
| pcl::SIFTKeypointFieldSelector< PointXYZRGBA > | |
| pcl::surface::SimplificationRemoveUnusedVertices | |
| pcl::StaticRangeCoder | StaticRangeCoder compression class |
| pcl::StopWatch | Simple stopwatch |
| pcl::ScopeTime | Class to measure the time spent in a scope |
| pcl::Synchronizer< T1, T2 > | /brief This template class synchronizes two data streams of different types |
| pcl::io::TARHeader | A TAR file's header, as described on http://en.wikipedia.org/wiki/Tar_%28file_format%29 |
| pcl::TexMaterial | |
| pcl::TextureMapping< PointInT > | The texture mapping algorithm |
| pcl::TextureMesh | |
| pcl::console::TicToc | |
| pcl::TimeTrigger | Timer 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::TransformationEstimationPointToPlane< PointSource, PointTarget > | TransformationEstimationPointToPlane uses Levenberg Marquardt optimization to find the transformation that minimizes the point-to-plane distance between the given correspondences |
| pcl::registration::TransformationEstimationPointToPlaneLLS< PointSource, PointTarget > | TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals |
| pcl::registration::TransformationEstimationSVD< PointSource, PointTarget > | TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences |
| pcl::TransformationFromCorrespondences | Calculates a transformation based on corresponding 3D points |
| pcl::registration::TransformationValidation< PointSource, PointTarget > | TransformationValidation represents the base class for methods that validate the correctness of a transformation found through TransformationEstimation |
| pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget > | TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset |
| pcl::io::ply::type_traits< ScalarType > | |
| pcl::texture_mapping::UvIndex | Structure that links a uv coordinate to its 3D point and face |
| pcl::VectorAverage< real, dimension > | Calculates the weighted average and the covariance matrix |
| pcl::registration::ELCH< PointT >::Vertex | |
| pcl::Vertices | Describes a set of vertices in a polygon mesh, by basically storing an array of indices |
| pcl::VFHSignature308 | A point structure representing the Viewpoint Feature Histogram (VFH) |
| vtkCommand | |
| pcl::visualization::FPSCallback | |
| pcl::visualization::PointPickingCallback | |
| vtkImageCanvasSource2D | |
| pcl::visualization::PCLImageCanvasSource2D | PclImageCanvasSource2D represents our own custom version of vtkImageCanvasSource2D, used by the ImageViewer class |
| vtkInteractorStyleTrackballCamera | |
| pcl::visualization::PCLHistogramVisualizerInteractorStyle | PCL histogram visualizer interactory style class |
| pcl::visualization::PCLVisualizerInteractorStyle | PCLVisualizerInteractorStyle defines an unique, custom VTK based interactory style for PCL Visualizer applications |
| pcl::VTKUtils | |
| vtkXRenderWindowInteractor | |
| pcl::visualization::PCLVisualizerInteractor | The PCLVisualizer interactor |
| pcl::WarpPointRigid< PointSourceT, PointTargetT > | |
| pcl::WarpPointRigid3D< PointSourceT, PointTargetT > | |
| pcl::WarpPointRigid6D< PointSourceT, PointTargetT > | |
| pcl::visualization::Window | |
| pcl::xNdCopyEigenPointFunctor< PointT > | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |
| pcl::xNdCopyPointEigenFunctor< PointT > | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |
| OctreeT | |
| pcl::octree::OctreePointCloud< PointT, LeafT, BranchT, OctreeT > | Octree pointcloud class |
| pcl::octree::OctreePointCloudPointVector< PointT, LeafT, BranchT, OctreeT > | Octree pointcloud point vector class |
| pcl::octree::OctreePointCloudSinglePoint< PointT, LeafT, BranchT, OctreeT > | Octree pointcloud single point class |
| pcl::octree::PointCloudCompression< PointT, LeafT, BranchT, OctreeT > | Octree pointcloud compression class |
1.8.3.1
