| pcl::_Axis | |
| pcl::_Intensity | |
| pcl::_Intensity32u | |
| pcl::_Intensity8u | |
| pcl::_Normal | |
| pcl::tracking::_ParticleXYR | |
| pcl::tracking::_ParticleXYRP | |
| pcl::tracking::_ParticleXYRPY | |
| pcl::tracking::_ParticleXYZR | |
| pcl::tracking::_ParticleXYZRPY | |
| pcl::_PointNormal | |
| pcl::_PointSurfel | |
| pcl::_PointWithRange | |
| pcl::_PointWithScale | |
| pcl::_PointWithViewpoint | |
| pcl::_PointXYZ | |
| pcl::_PointXYZHSV | |
| pcl::_PointXYZI | A point structure representing Euclidean xyz coordinates, and the intensity value |
| pcl::_PointXYZINormal | |
| pcl::_PointXYZL | |
| pcl::_PointXYZRGB | |
| pcl::_PointXYZRGBA | |
| pcl::_PointXYZRGBL | |
| pcl::_PointXYZRGBNormal | |
| pcl::_ReferenceFrame | A structure representing the Local Reference Frame of a point |
| pcl::_RGB | |
| pcl::keypoints::agast::AbstractAgastDetector | Abstract detector class for AGAST corner point detectors |
| AbstractMetadata | Abstract interface for outofcore metadata file types |
| pcl::AdaptiveRangeCoder | AdaptiveRangeCoder compression class |
| pcl::keypoints::internal::AgastApplyNonMaxSuppresion< Out > | |
| pcl::keypoints::internal::AgastApplyNonMaxSuppresion< pcl::PointUV > | |
| pcl::keypoints::internal::AgastDetector< Out > | |
| pcl::keypoints::agast::AgastDetector5_8 | Detector class for AGAST corner point detector (5_8) |
| pcl::keypoints::agast::AgastDetector7_12s | Detector class for AGAST corner point detector (7_12s) |
| pcl::keypoints::internal::AgastDetector< pcl::PointUV > | |
| pcl::AgastKeypoint2D< PointInT, PointOutT > | Detects 2D AGAST corner points |
| pcl::AgastKeypoint2D< pcl::PointXYZ, pcl::PointUV > | Detects 2D AGAST corner points |
| pcl::AgastKeypoint2DBase< PointInT, PointOutT, IntensityT > | Detects 2D AGAST corner points |
| pcl::poisson::Allocator< T > | This templated class assists in memory allocation and is well suited for instances when it is known that the sequence of memory allocations is performed in a stack-based manner, so that memory allocated last is released first |
| pcl::poisson::AllocatorState | |
| pcl::ApproximateVoxelGrid< PointT > | ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| pcl::tracking::ApproxNearestPairPointCloudCoherence< PointInT > | ApproxNearestPairPointCloudCoherence computes coherence between two pointclouds using the approximate nearest point pairs |
| pcl::visualization::AreaPickingEvent | /brief Class representing 3D area picking events |
| pcl::FastBilateralFilter< PointT >::Array3D | |
| pcl::ASCIIReader | Ascii Point Cloud Reader |
| 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< pcl::PCLPointField::FLOAT32 > | |
| pcl::traits::asType< pcl::PCLPointField::FLOAT64 > | |
| pcl::traits::asType< pcl::PCLPointField::INT16 > | |
| pcl::traits::asType< pcl::PCLPointField::INT32 > | |
| pcl::traits::asType< pcl::PCLPointField::INT8 > | |
| pcl::traits::asType< pcl::PCLPointField::UINT16 > | |
| pcl::traits::asType< pcl::PCLPointField::UINT32 > | |
| pcl::traits::asType< pcl::PCLPointField::UINT8 > | |
| Axes | |
| pcl::Axis | A point structure representing an Axis using its normal coordinates |
| BFGS< FunctorType > | BFGS stands for Broyden–Fletcher–Goldfarb–Shanno (BFGS) method for solving unconstrained nonlinear optimization problems |
| BFGSDummyFunctor< _Scalar, NX > | |
| pcl::BilateralFilter< PointT > | A bilateral filter implementation for point cloud data |
| pcl::BilateralUpsampling< PointInT, PointOutT > | Bilateral filtering implementation, based on the following paper: * Kopf, Johannes and Cohen, Michael F |
| pcl::poisson::BinaryNode< Real > | |
| pcl::BivariatePolynomialT< real > | This represents a bivariate polynomial and provides some functionality for it |
| pcl::BOARDLocalReferenceFrameEstimation< PointInT, PointNT, PointOutT > | BOARDLocalReferenceFrameEstimation implements the BOrder Aware Repeatable Directions algorithm for local reference frame estimation as described here: |
| pcl::BorderDescription | A structure to store if a point in a range image lies on a border between an obstacle and the background |
| pcl::Boundary | A point structure representing a description of whether a point is lying on a surface boundary or not |
| pcl::BoundaryEstimation< PointInT, PointNT, PointOutT > | BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion |
| pcl::recognition::BVH< UserData >::BoundedObject | |
| pcl::BoundingBoxXYZ | |
| pcl::BoxClipper3D< PointT > | Implementation of a box clipper in 3D. Actually it allows affine transformations, thus any parallelepiped in general pose. The affine transformation is used to transform the point before clipping it using the unit cube centered at origin and with an extend of -1 to +1 in each dimension |
| pcl::segmentation::grabcut::BoykovKolmogorov | Boost implementation of Boykov and Kolmogorov's maxflow algorithm doesn't support negative flows which makes it inappropriate for this conext |
| pcl::search::BruteForce< PointT > | Implementation of a simple brute force search algorithm |
| pcl::poisson::BSplineData< Degree, Real >::BSplineComponents | |
| pcl::poisson::BSplineData< Degree, Real > | |
| pcl::poisson::BSplineElementCoefficients< Degree > | |
| pcl::poisson::BSplineElements< Degree > | |
| pcl::octree::BufferedBranchNode< ContainerT > | |
| pcl::recognition::BVH< UserData > | This class is an implementation of bounding volume hierarchies |
| Camera | |
| 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::io::CameraParameters | Basic camera parameters placeholder |
| pcl::ColorGradientDOTModality< PointInT >::Candidate | |
| pcl::ColorGradientModality< PointInT >::Candidate | Candidate for a feature (used in feature extraction methods) |
| pcl::ColorModality< PointInT >::Candidate | |
| pcl::SurfaceNormalModality< PointInT >::Candidate | Candidate for a feature (used in feature extraction methods) |
| pcl::visualization::context_items::Circle | |
| cJSON | |
| cJSON_Hooks | |
| pcl::Clipper3D< PointT > | Base class for 3D clipper objects |
| cloud_point_index_idx | |
| pcl::visualization::CloudActor | |
| OutofcoreCloud::CloudDataCacheItem | |
| pcl::common::CloudGenerator< PointT, GeneratorT > | |
| pcl::common::CloudGenerator< pcl::PointXY, GeneratorT > | |
| pcl::CloudIterator< PointT > | Iterator class for point clouds with or without given indices |
| pcl::CloudSurfaceProcessing< PointInT, PointOutT > | CloudSurfaceProcessing represents the base class for algorithms that takes a point cloud as input and produces a new output cloud that has been modified towards a better surface representation |
| pcl::visualization::CloudViewer | Simple point cloud visualization class |
| pcl::segmentation::grabcut::Color | Structure to save RGB colors into floats |
| pcl::octree::ColorCoding< PointT > | ColorCoding class |
| pcl::ColorGradientDOTModality< PointInT > | |
| pcl::ColorGradientModality< PointInT > | Modality based on max-RGB gradients |
| pcl::ColorModality< PointInT > | |
| pcl::Comparator< PointT > | Comparator is the base class for comparators that compare two points given some function |
| pcl::keypoints::agast::AbstractAgastDetector::CompareScoreIndex | Score index comparator |
| pcl::ComparisonBase< PointT > | The (abstract) base class for the comparison object |
| pcl::io::CompressionPointTraits< PointT > | |
| pcl::io::CompressionPointTraits< PointXYZRGB > | |
| pcl::io::CompressionPointTraits< PointXYZRGBA > | |
| pcl::ComputeFailedException | |
| pcl::ConcaveHull< PointInT > | ConcaveHull (alpha shapes) using libqhull library |
| pcl::ConditionalEuclideanClustering< PointT > | ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined clustering condition |
| pcl::ConditionalRemoval< PointT > | ConditionalRemoval filters data that satisfies certain conditions |
| pcl::ConditionAnd< PointT > | AND condition |
| pcl::ConditionBase< PointT > | Base condition class |
| pcl::ConditionOr< PointT > | OR condition |
| pcl::io::configurationProfile_t | |
| pcl::ConstCloudIterator< PointT > | Iterator class for point clouds with or without given indices |
| pcl::ConstCloudIterator< PointT >::ConstIteratorIdx | |
| pcl::poisson::OctNode< NodeData, Real >::ConstNeighborKey3 | |
| pcl::poisson::OctNode< NodeData, Real >::ConstNeighborKey5 | |
| pcl::poisson::OctNode< NodeData, Real >::ConstNeighbors3 | |
| pcl::poisson::OctNode< NodeData, Real >::ConstNeighbors5 | |
| boost::container_gen< eigen_listS, ValueType > | |
| boost::container_gen< eigen_vecS, ValueType > | |
| pcl::registration::ConvergenceCriteria | ConvergenceCriteria represents an abstract base class for different convergence criteria used in registration loops |
| pcl::ConvexHull< PointInT > | ConvexHull using libqhull library |
| pcl::filters::Convolution< PointIn, PointOut > | Convolution is a mathematical operation on two functions f and g, producing a third function that is typically viewed as a modified version of one of the original functions |
| pcl::filters::Convolution3D< PointIn, PointOut, KernelT > | Convolution3D handles the non organized case where width and height are unknown or if you are only interested in convolving based on local neighborhood information |
| pcl::filters::ConvolvingKernel< PointInT, PointOutT > | Class ConvolvingKernel base class for all convolving kernels |
| pcl::filters::ConvolvingKernel< PointT, pcl::Normal > | |
| pcl::filters::ConvolvingKernel< PointT, pcl::PointXY > | |
| pcl::CopyIfFieldExists< PointInT, OutT > | A helper functor that can copy a specific value if the given field exists |
| pcl::poisson::CoredEdgeIndex | |
| pcl::poisson::CoredFileMeshData | |
| pcl::poisson::CoredFileMeshData2 | |
| pcl::poisson::CoredMeshData | |
| pcl::poisson::CoredMeshData2 | |
| pcl::poisson::CoredPointIndex | |
| pcl::poisson::CoredVectorMeshData | |
| pcl::poisson::CoredVectorMeshData2 | |
| pcl::poisson::CoredVertexIndex | |
| pcl::poisson::SortedTreeNodes::CornerIndices | |
| pcl::poisson::SortedTreeNodes::CornerTableData | |
| pcl::Correspondence | Correspondence represents a match between two entities (e.g., points, descriptors, etc) |
| pcl::registration::CorrespondenceEstimation< PointSource, PointTarget, Scalar > | CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features |
| pcl::registration::CorrespondenceEstimationBackProjection< PointSource, PointTarget, NormalT, Scalar > | CorrespondenceEstimationBackprojection computes correspondences as points in the target cloud which have minimum |
| pcl::registration::CorrespondenceEstimationBase< PointSource, PointTarget, Scalar > | Abstract CorrespondenceEstimationBase class |
| pcl::registration::CorrespondenceEstimationNormalShooting< PointSource, PointTarget, NormalT, Scalar > | CorrespondenceEstimationNormalShooting computes correspondences as points in the target cloud which have minimum distance to normals computed on the input cloud |
| pcl::registration::CorrespondenceEstimationOrganizedProjection< PointSource, PointTarget, Scalar > | CorrespondenceEstimationOrganizedProjection computes correspondences by projecting the source point cloud onto the target point cloud using the camera intrinsic and extrinsic parameters |
| pcl::CorrespondenceGrouping< PointModelT, PointSceneT > | Abstract base class for Correspondence Grouping algorithms |
| pcl::registration::CorrespondenceRejectionOrganizedBoundary | Implements a simple correspondence rejection measure |
| 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::CorrespondenceRejectorPoly< SourceT, TargetT > | CorrespondenceRejectorPoly implements a correspondence rejection method that exploits low-level and pose-invariant geometric constraints between two point sets by forming virtual polygons of a user-specifiable cardinality on each model using the input correspondences |
| pcl::registration::CorrespondenceRejectorSampleConsensus< PointT > | CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers) |
| pcl::registration::CorrespondenceRejectorSampleConsensus2D< PointT > | CorrespondenceRejectorSampleConsensus2D implements a pixel-based 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::CovarianceSampling< PointT, PointNT > | Point Cloud sampling based on the 6D covariances |
| pcl::CrfNormalSegmentation< PointT > | |
| pcl::CRHAlignment< PointT, nbins_ > | CRHAlignment uses two Camera Roll Histograms (CRH) to find the roll rotation that aligns both views |
| pcl::CRHEstimation< PointInT, PointNT, PointOutT > | CRHEstimation estimates the Camera Roll Histogram (CRH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in:
- CAD-Model Recognition and 6 DOF Pose Estimation A
|
| pcl::CropBox< PointT > | CropBox is a filter that allows the user to filter all the data inside of a given box |
| pcl::CropBox< pcl::PCLPointCloud2 > | CropBox is a filter that allows the user to filter all the data inside of a given box |
| pcl::CropHull< PointT > | Filter points that lie inside or outside a 3D closed surface or 2D closed polygon, as generated by the ConvexHull or ConcaveHull classes |
| pcl::poisson::Cube | |
| pcl::CustomPointRepresentation< PointDefault > | CustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point |
| pcl::CVFHEstimation< PointInT, PointNT, PointOutT > | CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in:
- CAD-Model Recognition and 6 DOF Pose Estimation A
|
| pcl::recognition::ORROctree::Node::Data | |
| 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::registration::DataContainerInterface | DataContainerInterface provides a generic interface for computing correspondence scores between correspondent points in the input and target clouds |
| pcl::traits::datatype< PointT, Tag > | |
| pcl::io::DeBayer | Various debayering methods |
| pcl::traits::decomposeArray< T > | |
| pcl::ConstCloudIterator< PointT >::DefaultConstIterator | |
| pcl::registration::DefaultConvergenceCriteria< Scalar > | DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the following criteria for registration loop evaluation: |
| 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::DefaultIterator< PointT > | |
| pcl::geometry::DefaultMeshTraits< VertexDataT, HalfEdgeDataT, EdgeDataT, FaceDataT > | The mesh traits are used to set up compile time settings for the mesh |
| pcl::DefaultPointRepresentation< PointDefault > | DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types |
| pcl::DefaultPointRepresentation< FPFHSignature33 > | |
| pcl::DefaultPointRepresentation< Narf36 > | |
| pcl::DefaultPointRepresentation< NormalBasedSignature12 > | |
| pcl::DefaultPointRepresentation< PFHRGBSignature250 > | |
| pcl::DefaultPointRepresentation< PFHSignature125 > | |
| pcl::DefaultPointRepresentation< PointNormal > | |
| pcl::DefaultPointRepresentation< PointXYZ > | |
| pcl::DefaultPointRepresentation< PointXYZI > | |
| pcl::DefaultPointRepresentation< PPFSignature > | |
| pcl::DefaultPointRepresentation< ShapeContext1980 > | |
| pcl::DefaultPointRepresentation< SHOT1344 > | |
| pcl::DefaultPointRepresentation< SHOT352 > | |
| pcl::DefaultPointRepresentation< VFHSignature308 > | |
| pcl::DenseQuantizedMultiModTemplate | |
| pcl::DenseQuantizedSingleModTemplate | |
| openni_wrapper::DepthImage | This class provides methods to fill a depth or disparity image |
| pcl::LineRGBD< PointXYZT, PointRGBT >::Detection | A LineRGBD detection |
| openni_wrapper::OpenNIDriver::DeviceContext | |
| openni_wrapper::DeviceKinect | Concrete implementation of the interface OpenNIDevice for a MS Kinect device |
| openni_wrapper::DeviceONI | Concrete implementation of the interface OpenNIDevice for a virtual device playing back an ONI file |
| openni_wrapper::DevicePrimesense | Concrete implementation of the interface OpenNIDevice for a Primesense device |
| openni_wrapper::DeviceXtionPro | Concrete implementation of the interface OpenNIDevice for a Asus Xtion Pro device |
| pcl::DifferenceOfNormalsEstimation< PointInT, PointNT, PointOutT > | A Difference of Normals (DoN) scale filter implementation for point cloud data |
| pcl::DinastGrabber | Grabber for DINAST devices (i.e., IPA-1002, IPA-1110, IPA-2001) |
| pcl::visualization::context_items::Disk | |
| pcl::tracking::DistanceCoherence< PointInT > | DistanceCoherence computes coherence between two points from the distance between them |
| pcl::DistanceMap | Represents a distance map obtained from a distance transformation |
| pcl::DOTMOD | Template matching using the DOTMOD approach |
| pcl::DOTModality | |
| pcl::DOTMODDetection | |
| pcl::EarClipping | The ear clipping triangulation algorithm |
| pcl::poisson::Edge | |
| pcl::EdgeAwarePlaneComparator< PointT, PointNT > | EdgeAwarePlaneComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| pcl::geometry::EdgeIndex | Index used to access elements in the half-edge mesh |
| pcl::poisson::EdgeIndex | |
| pcl::poisson::SortedTreeNodes::EdgeIndices | |
| pcl::registration::LUM< PointT >::EdgeProperties | |
| pcl::poisson::SortedTreeNodes::EdgeTableData | |
| boost::eigen_listS | |
| boost::eigen_vecS | |
| pcl::registration::ELCH< PointT > | ELCH (Explicit Loop Closing Heuristic) class |
| pcl::EnergyMaps | Stores a set of energy maps |
| pcl::recognition::RotationSpaceCell::Entry | |
| pcl::search::OrganizedNeighbor< PointT >::Entry | |
| pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ErrorFunctor | |
| pcl::ESFEstimation< PointInT, PointOutT > | ESFEstimation estimates the ensemble of shape functions descriptors for a given point cloud dataset containing points |
| pcl::ESFSignature640 | A point structure representing the Ensemble of Shape Functions (ESF) |
| pcl::EuclideanClusterComparator< PointT, PointNT, PointLT > | EuclideanClusterComparator is a comparator used for finding clusters supported by planar surfaces |
| pcl::EuclideanClusterExtraction< PointT > | EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense |
| pcl::EuclideanPlaneCoefficientComparator< PointT, PointNT > | EuclideanPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| pcl::visualization::Window::ExitCallback | |
| pcl::visualization::ImageViewer::ExitCallback | |
| pcl::visualization::Window::ExitMainLoopTimerCallback | |
| pcl::visualization::ImageViewer::ExitMainLoopTimerCallback | |
| pcl::ExtractIndices< PointT > | ExtractIndices extracts a set of indices from a point cloud |
| pcl::ExtractIndices< pcl::PCLPointCloud2 > | ExtractIndices extracts a set of indices from a point cloud |
| 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::geometry::Face | A face is a closed loop of edges |
| pcl::geometry::FaceAroundFaceCirculator< MeshT > | Circulates clockwise around a face and returns an index to the face of the outer half-edge (the target) |
| pcl::geometry::FaceAroundVertexCirculator< MeshT > | Circulates counter-clockwise around a vertex and returns an index to the face of the outgoing half-edge (the target) |
| pcl::geometry::FaceIndex | Index used to access elements in the half-edge mesh |
| pcl::FastBilateralFilter< PointT > | Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper: * Sylvain Paris and FrŽdo Durand "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach" European Conference on Computer Vision (ECCV'06) |
| pcl::FastBilateralFilterOMP< PointT > | Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper: * Sylvain Paris and FrÂŽdo Durand "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach" European Conference on Computer Vision (ECCV'06) |
| pcl::Feature< PointInT, PointOutT > | Feature represents the base feature class |
| pcl::registration::CorrespondenceRejectorFeatures::FeatureContainer< FeatureT > | An inner class containing pointers to the source and target feature clouds and the parameters needed to perform the correspondence search |
| pcl::registration::CorrespondenceRejectorFeatures::FeatureContainerInterface | |
| pcl::FeatureFromLabels< PointInT, PointLT, PointOutT > | |
| pcl::FeatureFromNormals< PointInT, PointNT, PointOutT > | |
| pcl::Narf::FeaturePointRepresentation | |
| pcl::FeatureWithLocalReferenceFrames< PointInT, PointRFT > | FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which need a local reference frame at each input keypoint |
| pcl::visualization::FEllipticArc2D | Class for storing EllipticArc; every ellipse , circle are covered by this |
| pcl::detail::FieldAdder< PointT > | |
| pcl::FieldComparison< PointT > | The field-based specialization of the comparison object |
| pcl::traits::fieldList< PointT > | |
| pcl::detail::FieldMapper< PointT > | |
| pcl::detail::FieldMapping | |
| pcl::FieldMatches< PointT, Tag > | |
| pcl::visualization::Figure2D | Abstract class for storing figure information |
| pcl::FileGrabber< PointT > | FileGrabber provides a container-style interface for grabbers which operate on fixed-size input |
| pcl::FileReader | Point Cloud Data (FILE) file format reader interface |
| pcl::FileWriter | Point Cloud Data (FILE) file format writer |
| pcl::visualization::context_items::FilledRectangle | |
| pcl::Filter< PointT > | Filter represents the base filter class |
| pcl::Filter< pcl::PCLPointCloud2 > | Filter represents the base filter class |
| pcl::FilterIndices< PointT > | FilterIndices represents the base class for filters that are about binary point removal |
| pcl::FilterIndices< pcl::PCLPointCloud2 > | FilterIndices represents the base class for filters that are about binary point removal |
| pcl::search::FlannSearch< PointT, FlannDistance >::FlannIndexCreator | Helper class that creates a FLANN index from a given FLANN matrix |
| pcl::search::FlannSearch< PointT, FlannDistance > | search::FlannSearch is a generic FLANN wrapper class for the new search interface |
| 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::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::FPFHSignature33 | A point structure representing the Fast Point Feature Histogram (FPFH) |
| pcl::visualization::FPoints2D | Class for storing Points |
| pcl::visualization::FPolygon2D | Class for Polygon |
| pcl::visualization::FPolyLine2D | Class for PolyLine |
| pcl::visualization::FQuad2D | Class for storing Quads |
| pcl::FrustumCulling< PointT > | FrustumCulling filters points inside a frustum given by pose and field of view of the camera |
| pcl::poisson::FunctionData< Degree, Real > | |
| pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::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::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::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< _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::segmentation::grabcut::Gaussian | Gaussian structure |
| pcl::segmentation::grabcut::GaussianFitter | Helper class that fits a single Gaussian to color samples |
| pcl::filters::GaussianKernel< PointInT, PointOutT > | Gaussian kernel implementation interface Use this as implementation reference |
| pcl::GaussianKernel | Class GaussianKernel assembles all the method for computing, convolving, smoothing, gradients computing an image using a gaussian kernel |
| pcl::filters::GaussianKernelRGB< PointInT, PointOutT > | Gaussian kernel implementation interface with RGB channel handling Use this as implementation reference |
| 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::GeometricConsistencyGrouping< PointModelT, PointSceneT > | Class implementing a 3D correspondence grouping enforcing geometric consistency among feature correspondences |
| Geometry | |
| pcl::GFPFHEstimation< PointInT, PointLT, PointOutT > | GFPFHEstimation estimates the Global Fast Point Feature Histogram (GFPFH) descriptor for a given point cloud dataset containing points and labels |
| pcl::GFPFHSignature16 | A point structure representing the GFPFH descriptor with 16 bins |
| pcl::GlobalHypothesesVerification< ModelT, SceneT > | A hypothesis verification method proposed in "A Global Hypotheses Verification Method for 3D Object Recognition", A |
| pcl::segmentation::grabcut::GMM | |
| pcl::Grabber | Grabber interface for PCL 1.x device drivers |
| pcl::GrabCut< PointT > | Implementation of the GrabCut segmentation in "GrabCut — Interactive Foreground Extraction using Iterated Graph Cuts" by Carsten Rother, Vladimir Kolmogorov and Andrew Blake |
| pcl::GradientXY | A point structure representing Euclidean xyz coordinates, and the intensity value |
| pcl::registration::GraphHandler< GraphT > | GraphHandler class is a wrapper for a general SLAM graph The actual graph class must fulfil the following boost::graph concepts:
- BidirectionalGraph
- AdjacencyGraph
- VertexAndEdgeListGraph
- MutableGraph
|
| pcl::registration::GraphOptimizer< GraphT > | GraphOptimizer class; derive and specialize for each graph type |
| pcl::GraphRegistration< GraphT > | GraphRegistration class is the base class for graph-based registration methods |
| pcl::GreedyProjectionTriangulation< PointInT > | GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections |
| pcl::GreedyVerification< ModelT, SceneT > | A greedy hypothesis verification method |
| Grid | |
| pcl::GridProjection< PointNT > | Grid projection surface reconstruction method |
| pcl::people::GroundBasedPeopleDetectionApp< PointT > | |
| pcl::GroundPlaneComparator< PointT, PointNT > | GroundPlaneComparator is a Comparator for detecting smooth surfaces suitable for driving |
| pcl::geometry::HalfEdge | An edge is a connection between two vertices |
| pcl::geometry::HalfEdgeIndex | Index used to access elements in the half-edge mesh |
| pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT > | HarrisKeypoint2D detects Harris corners family 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::HarrisKeypoint6D< PointInT, PointOutT, NormalT > | Keypoint detector for detecting corners in 3D (XYZ), 2D (intensity) AND mixed versions of these |
| __gnu_cxx::hash< const long long > | |
| __gnu_cxx::hash< const unsigned long long > | |
| __gnu_cxx::hash< long long > | |
| __gnu_cxx::hash< unsigned long long > | |
| pcl::PPFHashMapSearch::HashKeyStruct | Data structure to hold the information for the key in the feature hash map of the PPFHashMapSearch class |
| pcl::HDLGrabber::HDLDataPacket | |
| pcl::HDLGrabber::HDLFiringData | |
| pcl::HDLGrabber | Grabber for the Velodyne High-Definition-Laser (HDL) |
| pcl::HDLGrabber::HDLLaserCorrection | |
| pcl::HDLGrabber::HDLLaserReturn | |
| pcl::people::HeadBasedSubclustering< PointT > | |
| pcl::people::HeightMap2D< PointT > | |
| pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT, NrDims > | |
| pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT[NrDims], NrDims > | |
| pcl::Histogram< N > | A point structure representing an N-D histogram |
| pcl::people::HOG | HOG represents a class for computing the HOG descriptor described in Dalal, N |
| pcl::Hough3DGrouping< PointModelT, PointSceneT, PointModelRfT, PointSceneRfT > | Class implementing a 3D correspondence grouping algorithm that can deal with multiple instances of a model template found into a given scene |
| pcl::recognition::HoughSpace3D | HoughSpace3D is a 3D voting space |
| pcl::tracking::HSVColorCoherence< PointInT > | HSVColorCoherence computes coherence between the two points from the color difference between them |
| pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::HuberPenalty | |
| pcl::recognition::Hypothesis | |
| pcl::recognition::HypothesisBase | |
| pcl::recognition::ObjRecRANSAC::HypothesisCreator | |
| pcl::HypothesisVerification< ModelT, SceneT > | Abstract class for hypotheses verification methods |
| openni_wrapper::Image | Image class containing just a reference to image meta data |
| openni_wrapper::ImageBayerGRBG | This class provides methods to fill a RGB or Grayscale image buffer from underlying Bayer pattern image |
| pcl::ImageGrabber< PointT > | |
| pcl::ImageGrabberBase | Base class for Image file grabber |
| openni_wrapper::ImageRGB24 | This class provides methods to fill a RGB or Grayscale image buffer from underlying RGB24 image |
| pcl::visualization::ImageViewer | ImageViewer is a class for 2D image visualization |
| pcl::visualization::ImageViewerInteractorStyle | An image viewer interactor style, tailored for ImageViewer |
| openni_wrapper::ImageYUV422 | Concrete implementation of the interface Image for a YUV 422 image used by Primesense devices |
| pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT > | This class implements Implicit Shape Model algorithm described in "Hough Transforms and 3D SURF for robust three dimensional classication" by Jan Knopp1, Mukta Prasad, Geert Willems1, Radu Timofte, and Luc Van Gool |
| pcl::geometry::IncomingHalfEdgeAroundVertexCirculator< MeshT > | Circulates counter-clockwise around a vertex and returns an index to the incoming half-edge (the target) |
| pcl::InitFailedException | An exception thrown when init can not be performed should be used in all the PCLBase class inheritants |
| pcl::geometry::InnerHalfEdgeAroundFaceCirculator< MeshT > | Circulates clockwise around a face and returns an index to the inner half-edge (the target) |
| pcl::IntegralImage2D< DataType, Dimension > | Determines an integral image representation for a given organized data array |
| pcl::IntegralImage2D< DataType, 1 > | Partial template specialization for integral images with just one channel |
| pcl::IntegralImageNormalEstimation< PointInT, PointOutT > | Surface normal estimation on organized data using integral images |
| pcl::IntegralImageTypeTraits< DataType > | |
| pcl::IntegralImageTypeTraits< char > | |
| pcl::IntegralImageTypeTraits< float > | |
| pcl::IntegralImageTypeTraits< int > | |
| pcl::IntegralImageTypeTraits< short > | |
| pcl::IntegralImageTypeTraits< unsigned char > | |
| pcl::IntegralImageTypeTraits< unsigned int > | |
| pcl::IntegralImageTypeTraits< unsigned short > | |
| pcl::Intensity | A point structure representing the grayscale intensity in single-channel images |
| pcl::Intensity32u | A point structure representing the grayscale intensity in single-channel images |
| pcl::Intensity8u | A point structure representing the grayscale intensity in single-channel images |
| pcl::common::IntensityFieldAccessor< PointT > | |
| pcl::common::IntensityFieldAccessor< pcl::PointNormal > | |
| pcl::common::IntensityFieldAccessor< pcl::PointXYZ > | |
| pcl::common::IntensityFieldAccessor< pcl::PointXYZRGB > | |
| pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBA > | |
| pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBL > | |
| pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBNormal > | |
| pcl::IntensityGradient | A point structure representing the intensity gradient of an XYZI point cloud |
| pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT > | IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values |
| pcl::IntensitySpinEstimation< PointInT, PointOutT > | 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< Sequence1, Sequence2 > | |
| pcl::InvalidConversionException | An exception that is thrown when a PCLPointCloud2 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 |
| boost::detail::is_random_access< eigen_listS > | |
| boost::detail::is_random_access< eigen_vecS > | |
| pcl::PosesFromMatches::PoseEstimate::IsBetter | |
| pcl::features::ISMModel | The assignment of this structure is to store the statistical/learned weights and other information of the trained Implict Shape Model algorithm |
| pcl::ISMPeak | This struct is used for storing peak |
| pcl::features::ISMVoteList< PointT > | This class is used for storing, analyzing and manipulating votes obtained from ISM algorithm |
| pcl::IsNotDenseException | An exception that is thrown when a PointCloud is not dense but is attemped to be used as dense |
| pcl::ISSKeypoint3D< PointInT, PointOutT, NormalT > | ISSKeypoint3D detects the Intrinsic Shape Signatures keypoints for a given point cloud |
| pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar > | IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm |
| pcl::IterativeClosestPointNonLinear< PointSource, PointTarget, Scalar > | IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend |
| pcl::IterativeClosestPointWithNormals< PointSource, PointTarget, Scalar > | IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformation estimated based on Point to Plane distances by default |
| pcl::IteratorIdx< PointT > | |
| pcl::octree::IteratorState | |
| pcl::search::KdTree< PointT > | search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure |
| 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::search::FlannSearch< PointT, FlannDistance >::KdTreeIndexCreator | Creates a FLANN KdTreeSingleIndex from the given input data |
| pcl::KernelWidthTooSmallException | An exception that is thrown when the kernel size is too small |
| pcl::visualization::KeyboardEvent | /brief Class representing key hit/release events |
| pcl::Keypoint< PointInT, PointOutT > | Keypoint represents the base class for key points |
| kiss_fft_cpx | |
| kiss_fft_state | |
| pcl::tracking::KLDAdaptiveParticleFilterOMPTracker< PointInT, StateT > | KLDAdaptiveParticleFilterOMPTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method |
| pcl::tracking::KLDAdaptiveParticleFilterTracker< PointInT, StateT > | KLDAdaptiveParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method |
| pcl::search::FlannSearch< PointT, FlannDistance >::KMeansIndexCreator | Creates a FLANN KdTreeSingleIndex from the given input data |
| pcl::Label | |
| pcl::LabeledEuclideanClusterExtraction< PointT > | LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info |
| pcl::VoxelGridCovariance< PointT >::Leaf | Simple structure to hold a centroid, covarince and the number of points in a leaf |
| pcl::GridProjection< PointNT >::Leaf | Data leaf |
| pcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGrid::Leaf | |
| pcl::UniformSampling< PointInT >::Leaf | Simple structure to hold an nD centroid and the number of points in a leaf |
| pcl::LeastMedianSquares< PointT > | LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm |
| pcl::visualization::context_items::Line | |
| pcl::LinearizedMaps | Stores a set of linearized maps |
| pcl::LinearLeastSquaresNormalEstimation< PointInT, PointOutT > | Surface normal estimation on dense data using a least-squares estimation based on a first-order Taylor approximation |
| pcl::LineIterator | Organized Index Iterator for iterating over the "pixels" for a given line using the Bresenham algorithm |
| pcl::LINEMOD | Template matching using the LINEMOD approach |
| pcl::LINEMOD_OrientationMap | Map that stores orientations |
| pcl::LINEMODDetection | Represents a detection of a template using the LINEMOD approach |
| pcl::LineRGBD< PointXYZT, PointRGBT > | High-level class for template matching using the LINEMOD approach based on RGB and Depth data |
| pcl::io::ply::ply_parser::list_property_begin_callback_type< SizeType, ScalarType > | |
| pcl::io::ply::ply_parser::list_property_definition_callback_type< SizeType, ScalarType > | |
| pcl::io::ply::ply_parser::list_property_definition_callbacks_type | |
| pcl::io::ply::ply_parser::list_property_element_callback_type< SizeType, ScalarType > | |
| pcl::io::ply::ply_parser::list_property_end_callback_type< SizeType, ScalarType > | |
| pcl::RangeImageBorderExtractor::LocalSurface | Stores some information extracted from the neighborhood of a point |
| pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::LocationInfo | This structure stores the information about the keypoint |
| LRUCache< KeyT, CacheItemT > | |
| LRUCacheItem< T > | |
| pcl::registration::LUM< PointT > | Globally Consistent Scan Matching based on an algorithm by Lu and Milios |
| pcl::io::LZFBayer8ImageReader | PCL-LZF 8-bit Bayer image format reader |
| pcl::io::LZFBayer8ImageWriter | PCL-LZF 8-bit Bayer image format writer |
| pcl::io::LZFDepth16ImageReader | PCL-LZF 16-bit depth image format reader |
| pcl::io::LZFDepth16ImageWriter | PCL-LZF 16-bit depth image format writer |
| pcl::io::LZFImageReader | PCL-LZF image format reader |
| pcl::io::LZFImageWriter | PCL-LZF image format writer |
| pcl::io::LZFRGB24ImageReader | PCL-LZF 24-bit RGB image format reader |
| pcl::io::LZFRGB24ImageWriter | PCL-LZF 24-bit RGB image format writer |
| pcl::io::LZFYUV422ImageReader | PCL-LZF 8-bit Bayer image format reader |
| pcl::io::LZFYUV422ImageWriter | PCL-LZF 16-bit YUV422 image format writer |
| pcl::poisson::MapReduceVector< T2 > | |
| pcl::poisson::MarchingCubes | |
| 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::MarchingSquares | |
| pcl::MaskMap | |
| pcl::poisson::MatrixEntry< T > | |
| pcl::MaximumLikelihoodSampleConsensus< PointT > | MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to
estimating image geometry", P.H.S |
| pcl::MedianFilter< PointT > | Implementation of the median filter |
| Mesh | |
| pcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT > | Base class for the half-edge mesh |
| pcl::MeshConstruction< PointInT > | MeshConstruction represents a base surface reconstruction class |
| pcl::geometry::MeshIO< MeshT > | Read / write the half-edge mesh from / to a file |
| pcl::MeshProcessing | MeshProcessing represents the base class for mesh processing algorithms |
| pcl::MeshQuadricDecimationVTK | PCL mesh decimation based on vtkQuadricDecimation from the VTK library |
| 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::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::poisson::MinimalAreaTriangulation< Real > | |
| pcl::MovingLeastSquares< PointInT, PointOutT >::MLSResult | Data structure used to store the results of the MLS fitting |
| pcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGrid | A minimalistic implementation of a voxel grid, necessary for the point cloud upsampling |
| pcl::OpenNIGrabber::modeComp | |
| pcl::recognition::ModelLibrary::Model | Stores some information about the model |
| pcl::ModelCoefficients | |
| pcl::recognition::ModelLibrary | |
| pcl::MomentInvariants | A point structure representing the three moment invariants |
| pcl::MomentInvariantsEstimation< PointInT, PointOutT > | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |
| MonitorQueue< DataT > | |
| pcl::visualization::MouseEvent | |
| pcl::MovingLeastSquares< PointInT, PointOutT > | MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation |
| 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::TransformationValidationEuclidean< PointSource, PointTarget, Scalar >::MyPointRepresentation | Internal point representation uses only 3D coordinates for L2 |
| 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::NarfDescriptor | Computes NARF feature descriptors for points in a range image See B |
| pcl::NarfKeypoint | NARF (Normal Aligned Radial Feature) keypoints |
| pcl::NdCentroidFunctor< PointT, Scalar > | 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 |
| pcl::ndt2d::NDT2D< PointT > | Build a Normal Distributions Transform of a 2D point cloud |
| pcl::ndt2d::NDTSingleGrid< PointT > | Build a set of normal distributions modelling a 2D point cloud, and provide the value and derivatives of the model at any point via the test ( |
| pcl::tracking::NearestPairPointCloudCoherence< PointInT > | NearestPairPointCloudCoherence computes coherence between two pointclouds using the nearest point pairs |
| pcl::poisson::OctNode< NodeData, Real >::NeighborKey3 | |
| pcl::poisson::OctNode< NodeData, Real >::NeighborKey5 | |
| pcl::poisson::OctNode< NodeData, Real >::Neighbors3 | |
| pcl::poisson::OctNode< NodeData, Real >::Neighbors5 | |
| pcl::GrabCut< PointT >::NLinks | |
| pcl::geometry::NoData | No data is associated with the vertices / half-edges / edges / faces |
| pcl::recognition::BVH< UserData >::Node | |
| pcl::recognition::ORRGraph< NodeData >::Node | |
| pcl::recognition::ORROctree::Node | |
| pcl::recognition::SimpleOctree< NodeData, NodeDataCreator, Scalar >::Node | |
| pcl::Normal | A point structure representing normal coordinates and the surface curvature estimate |
| pcl::common::normal_distribution< T > | Normal distribution |
| pcl::NormalBasedSignature12 | A point structure representing the Normal Based Signature for a feature matrix of 4-by-3 |
| pcl::NormalBasedSignatureEstimation< PointT, PointNT, PointFeature > | Normal-based feature signature estimation class |
| pcl::tracking::NormalCoherence< PointInT > | NormalCoherence computes coherence between two points from the angle between their normals |
| pcl::ndt2d::NormalDist< PointT > | A normal distribution estimation class |
| pcl::NormalDistributionsTransform< PointSource, PointTarget > | A 3D Normal Distribution Transform registration implementation for point cloud data |
| pcl::NormalDistributionsTransform2D< PointSource, PointTarget > | NormalDistributionsTransform2D provides an implementation of the Normal Distributions Transform algorithm for scan matching |
| 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::common::NormalGenerator< T > | NormalGenerator class generates a random number from a normal distribution specified by (mean, sigma) |
| pcl::NormalRefinement< NormalT > | Normal vector refinement class |
| pcl::NormalSpaceSampling< PointT, NormalT > | NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every point |
| pcl::NotEnoughPointsException | An exception that is thrown when the number of correspondants is not equal to the minimum required |
| pcl::registration::NullEstimate | NullEstimate struct |
| pcl::registration::NullMeasurement | NullMeasurement struct |
| Eigen::NumTraits< pcl::ndt2d::NormalDist< PointT > > | |
| pcl::poisson::NVector< T, Dim > | |
| pcl::keypoints::agast::OastDetector9_16 | Detector class for AGAST corner point detector (OAST 9_16) |
| Object | |
| ObjectFeatures | |
| ObjectModel | |
| ObjectRecognition | |
| ObjectRecognitionParameters | |
| pcl::recognition::ObjRecRANSAC | This is a RANSAC-based 3D object recognition method |
| pcl::poisson::OctNode< NodeData, Real > | |
| pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT > | search::Octree is a wrapper class which implements nearest neighbor search operations based on the pcl::octree::Octree structure |
| pcl::poisson::Octree< Degree > | |
| pcl::octree::Octree2BufBase< LeafContainerT, BranchContainerT > | Octree double buffer class |
| pcl::octree::OctreeBase< LeafContainerT, BranchContainerT > | Octree class |
| pcl::octree::OctreeBranchNode< ContainerT > | Abstract octree branch class |
| pcl::octree::OctreeBreadthFirstIterator< OctreeT > | Octree iterator class |
| pcl::octree::OctreeContainerBase | Octree container class that can serve as a base to construct own leaf node container classes |
| pcl::octree::OctreeContainerEmpty | Octree container class that does not store any information |
| pcl::octree::OctreeContainerPointIndex | Octree container class that does store a single point index |
| pcl::octree::OctreeContainerPointIndices | Octree container class that does store a vector of point indices |
| pcl::octree::OctreeDepthFirstIterator< OctreeT > | Octree iterator class |
| pcl::octree::OctreeIteratorBase< OctreeT > | Abstract octree iterator class |
| pcl::octree::OctreeKey | Octree key class |
| pcl::octree::OctreeLeafNode< ContainerT > | Abstract octree leaf class |
| pcl::octree::OctreeLeafNodeIterator< OctreeT > | Octree leaf node iterator class |
| pcl::octree::OctreeNode | Abstract octree node class |
| pcl::octree::OctreeNodePool< NodeT > | Octree node pool |
| pcl::octree::OctreePointCloud< PointT, LeafContainerT, BranchContainerT, OctreeT > | Octree pointcloud class |
| pcl::octree::OctreePointCloudAdjacency< PointT, LeafContainerT, BranchContainerT > | Octree pointcloud voxel class used for adjacency calculation |
| pcl::octree::OctreePointCloudAdjacencyContainer< PointInT, DataT > | Octree adjacency leaf container class- stores set of pointers to neighbors, number of points added, and a DataT value |
| pcl::octree::OctreePointCloudChangeDetector< PointT, LeafContainerT, BranchContainerT > | Octree pointcloud change detector class |
| pcl::io::OctreePointCloudCompression< PointT, LeafT, BranchT, OctreeT > | Octree pointcloud compression class |
| pcl::octree::OctreePointCloudDensity< PointT, LeafContainerT, BranchContainerT > | Octree pointcloud density class |
| pcl::octree::OctreePointCloudDensityContainer | Octree pointcloud density leaf node class |
| pcl::octree::OctreePointCloudOccupancy< PointT, LeafContainerT, BranchContainerT > | Octree pointcloud occupancy class |
| pcl::octree::OctreePointCloudPointVector< PointT, LeafContainerT, BranchContainerT, OctreeT > | Octree pointcloud point vector class |
| pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT > | Octree pointcloud search class |
| pcl::octree::OctreePointCloudSinglePoint< PointT, LeafContainerT, BranchContainerT, OctreeT > | Octree pointcloud single point class |
| pcl::octree::OctreePointCloudVoxelCentroid< PointT, LeafContainerT, BranchContainerT > | Octree pointcloud voxel centroid class |
| pcl::octree::OctreePointCloudVoxelCentroidContainer< PointT > | Octree pointcloud voxel centroid leaf node class |
| OctreeT | |
| pcl::traits::offset< PointT, Tag > | |
| pcl::ONIGrabber | A simple ONI grabber |
| OpenNICapture | |
| openni_wrapper::OpenNIDevice | Class representing an astract device for OpenNI devices: Primesense PSDK, Microsoft Kinect, Asus Xtion Pro/Live |
| openni_wrapper::OpenNIDriver | Driver class implemented as Singleton |
| openni_wrapper::OpenNIException | General exception class |
| pcl::OpenNIGrabber | Grabber for OpenNI devices (i.e., Primesense PSDK, Microsoft Kinect, Asus XTion Pro/Live) |
| pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctor | |
| pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctor | |
| pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices | Optimization functor structure |
| pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices | |
| pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices | |
| pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT > | OrganizedConnectedComponentSegmentation allows connected components to be found within organized point cloud data, given a comparison function |
| pcl::io::OrganizedConversion< PointT, false > | |
| pcl::io::OrganizedConversion< PointT, true > | |
| pcl::OrganizedFastMesh< PointInT > | Simple triangulation/surface reconstruction for organized point clouds |
| pcl::OrganizedIndexIterator | Base class for iterators on 2-dimensional maps like images/organized clouds etc |
| pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT > | OrganizedMultiPlaneSegmentation finds all planes present in the input cloud, and outputs a vector of plane equations, as well as a vector of point clouds corresponding to the inliers of each detected plane |
| pcl::search::OrganizedNeighbor< PointT > | OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds |
| pcl::io::OrganizedPointCloudCompression< PointT > | |
| pcl::recognition::ObjRecRANSAC::OrientedPointPair | |
| pcl::recognition::ORRGraph< NodeData > | |
| pcl::recognition::ORROctree | That's a very specialized and simple octree class |
| pcl::recognition::ORROctreeZProjection | |
| pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT > | OURCVFHEstimation estimates the Oriented, Unique and Repetable Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset given XYZ data and normals, as presented in:
- OUR-CVFH – Oriented, Unique and Repeatable Clustered Viewpoint Feature Histogram for Object Recognition and 6DOF Pose Estimation A
|
| pcl::geometry::OuterHalfEdgeAroundFaceCirculator< MeshT > | Circulates clockwise around a face and returns an index to the outer half-edge (the target) |
| pcl::geometry::OutgoingHalfEdgeAroundVertexCirculator< MeshT > | Circulates counter-clockwise around a vertex and returns an index to the outgoing half-edge (the target) |
| pcl::outofcore::OutofcoreAbstractMetadata | |
| pcl::outofcore::OutofcoreAbstractNodeContainer< PointT > | |
| pcl::outofcore::OutofcoreBreadthFirstIterator< PointT, ContainerT > | |
| OutofcoreCloud | |
| pcl::outofcore::OutofcoreDepthFirstIterator< PointT, ContainerT > | |
| pcl::outofcore::OutofcoreIteratorBase< PointT, ContainerT > | Abstract octree iterator class |
| pcl::outofcore::OutofcoreOctreeBase< ContainerT, PointT > | This code defines the octree used for point storage at Urban Robotics |
| pcl::outofcore::OutofcoreOctreeBaseMetadata | Encapsulated class to read JSON metadata into memory, and write the JSON metadata associated with the octree root node |
| pcl::outofcore::OutofcoreOctreeBaseNode< ContainerT, PointT > | OutofcoreOctreeBaseNode Class internally representing nodes of an outofcore octree, with accessors to its data via the octree_disk_container class or octree_ram_container class, whichever it is templated against |
| pcl::outofcore::OutofcoreOctreeDiskContainer< PointT > | Class responsible for serialization and deserialization of out of core point data |
| pcl::outofcore::OutofcoreOctreeNodeMetadata | Encapsulated class to read JSON metadata into memory, and write the JSON metadata for each node |
| pcl::outofcore::OutofcoreOctreeRamContainer< PointT > | Storage container class which the outofcore octree base is templated against |
| pcl::outofcore::OutofcoreParams | |
| pcl::recognition::ObjRecRANSAC::Output | This is an output item of the ObjRecRANSAC::recognize() method |
| pcl::PackedHSIComparison< PointT > | A packed HSI specialization of the comparison object |
| pcl::PackedRGBComparison< PointT > | A packed rgb specialization of the comparison object |
| pcl::PairwiseGraphRegistration< GraphT, PointT > | PairwiseGraphRegistration class aligns the clouds two by two |
| pcl::PapazovHV< ModelT, SceneT > | A hypothesis verification method proposed in "An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes", C |
| boost::parallel_edge_traits< eigen_listS > | |
| boost::parallel_edge_traits< eigen_vecS > | |
| pcl::common::UniformGenerator< T >::Parameters | |
| pcl::common::NormalGenerator< T >::Parameters | |
| pcl::RangeImageBorderExtractor::Parameters | Parameters used in this class |
| BFGS< FunctorType >::Parameters | |
| pcl::NarfKeypoint::Parameters | Parameters used in this class |
| pcl::PolynomialCalculationsT< real >::Parameters | Parameters used in this class |
| pcl::PosesFromMatches::Parameters | Parameters used in this class |
| pcl::NarfDescriptor::Parameters | |
| pcl::tracking::ParticleFilterOMPTracker< PointInT, StateT > | ParticleFilterOMPTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method in parallel, using the OpenMP standard |
| pcl::tracking::ParticleFilterTracker< PointInT, StateT > | ParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method |
| pcl::tracking::ParticleXYR | |
| pcl::tracking::ParticleXYRP | |
| pcl::tracking::ParticleXYRPY | |
| pcl::tracking::ParticleXYZR | |
| pcl::tracking::ParticleXYZRPY | |
| pcl::PassThrough< PointT > | PassThrough passes points in a cloud based on constraints for one particular field of the point type |
| pcl::PassThrough< pcl::PCLPointCloud2 > | PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints |
| pcl::PCA< PointT > | Principal Component analysis (PCA) class |
| pcl::PCDGrabber< PointT > | |
| pcl::PCDGrabberBase | Base class for PCD file grabber |
| OutofcoreCloud::PcdQueueItem | |
| pcl::PCDReader | Point Cloud Data (PCD) file format reader |
| pcl::PCDWriter | Point Cloud Data (PCD) file format writer |
| pcl::PCLBase< PointT > | PCL base class |
| pcl::PCLBase< pcl::PCLPointCloud2 > | |
| pcl::visualization::PCLContextImageItem | Struct PCLContextImageItem a specification of vtkContextItem, used to add an image to the scene in the ImageViewer class |
| pcl::visualization::PCLContextItem | Struct PCLContextItem represents our own custom version of vtkContextItem, used by the ImageViewer class |
| pcl::PCLException | A base class for all pcl exceptions which inherits from std::runtime_error |
| pcl::PCLHeader | |
| pcl::visualization::PCLHistogramVisualizer | PCL histogram visualizer main class |
| pcl::visualization::PCLHistogramVisualizerInteractorStyle | PCL histogram visualizer interactory style class |
| pcl::PCLImage | |
| pcl::visualization::PCLImageCanvasSource2D | PCLImageCanvasSource2D represents our own custom version of vtkImageCanvasSource2D, used by the ImageViewer class |
| pcl::visualization::PCLPainter2D | PCL Painter2D main class |
| pcl::visualization::PCLPlotter | PCL Plotter main class |
| pcl::PCLPointCloud2 | |
| pcl::PCLPointField | |
| pcl::visualization::PCLSimpleBufferVisualizer | PCL simple buffer visualizer main class |
| pcl::PCLSurfaceBase< PointInT > | Pure abstract class |
| pcl::visualization::PCLVisualizer | PCL Visualizer main class |
| pcl::visualization::PCLVisualizerInteractor | The PCLVisualizer interactor |
| pcl::visualization::PCLVisualizerInteractorStyle | PCLVisualizerInteractorStyle defines an unique, custom VTK based interactory style for PCL Visualizer applications |
| pcl::people::PersonClassifier< PointT > | |
| pcl::people::PersonCluster< PointT > | |
| 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::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::recognition::ORROctreeZProjection::Pixel | |
| pcl::PlanarPolygon< PointT > | PlanarPolygon represents a planar (2D) polygon, potentially in a 3D space |
| pcl::PlanarPolygonFusion< PointT > | PlanarPolygonFusion takes a list of 2D planar polygons and attempts to reduce them to a minimum set that best represents the scene, based on various given comparators |
| pcl::PlanarRegion< PointT > | PlanarRegion represents a set of points that lie in a plane |
| pcl::PlaneClipper3D< PointT > | Implementation of a plane clipper in 3D |
| pcl::PlaneCoefficientComparator< PointT, PointNT > | PlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| pcl::PlaneRefinementComparator< PointT, PointNT, PointLT > | PlaneRefinementComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| pcl::io::ply::ply_parser | Class ply_parser parses a PLY file and generates appropriate atomic parsers for the body |
| pcl::PLYReader | Point Cloud Data (PLY) file format reader |
| pcl::PLYWriter | Point Cloud Data (PLY) file format writer |
| pcl::traits::POD< PointT > | |
| pcl::visualization::context_items::Point | |
| pcl::poisson::Point3D< Real > | |
| pcl::PointCloud< PointT > | PointCloud represents the base class in PCL for storing collections of 3D points |
| pcl::tracking::PointCloudCoherence< PointInT > | PointCloudCoherence is a base class to compute coherence between the two PointClouds |
| pcl::visualization::PointCloudColorHandler< PointT > | Base Handler class for PointCloud colors |
| pcl::visualization::PointCloudColorHandler< pcl::PCLPointCloud2 > | Base Handler class for PointCloud colors |
| pcl::visualization::PointCloudColorHandlerCustom< PointT > | Handler for predefined user colors |
| pcl::visualization::PointCloudColorHandlerCustom< pcl::PCLPointCloud2 > | Handler for predefined user colors |
| pcl::visualization::PointCloudColorHandlerGenericField< PointT > | Generic field handler class for colors |
| pcl::visualization::PointCloudColorHandlerGenericField< pcl::PCLPointCloud2 > | Generic field handler class for colors |
| pcl::visualization::PointCloudColorHandlerHSVField< PointT > | HSV handler class for colors |
| pcl::visualization::PointCloudColorHandlerHSVField< pcl::PCLPointCloud2 > | 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::PointCloudColorHandlerRandom< pcl::PCLPointCloud2 > | Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen) |
| pcl::visualization::PointCloudColorHandlerRGBField< PointT > | RGB handler class for colors |
| pcl::visualization::PointCloudColorHandlerRGBField< pcl::PCLPointCloud2 > | RGB handler class for colors |
| pcl::visualization::PointCloudGeometryHandler< PointT > | Base handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandler< pcl::PCLPointCloud2 > | Base handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerCustom< PointT > | Custom handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerCustom< pcl::PCLPointCloud2 > | Custom handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< PointT > | Surface normal handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< pcl::PCLPointCloud2 > | Surface normal handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerXYZ< PointT > | XYZ handler class for PointCloud geometry |
| pcl::visualization::PointCloudGeometryHandlerXYZ< pcl::PCLPointCloud2 > | XYZ handler class for PointCloud geometry |
| pcl::io::PointCloudImageExtractor< PointT > | Base Image Extractor class for organized point clouds |
| pcl::io::PointCloudImageExtractorFromCurvatureField< PointT > | Image Extractor which uses the data present in the "curvature" field to produce a curvature map (as a monochrome image with mono16 encoding) |
| pcl::io::PointCloudImageExtractorFromIntensityField< PointT > | Image Extractor which uses the data present in the "intensity" field to produce a monochrome intensity image (with mono16 encoding) |
| pcl::io::PointCloudImageExtractorFromLabelField< PointT > | Image Extractor which uses the data present in the "label" field to produce either monochrome or RGB image where different labels correspond to different colors |
| pcl::io::PointCloudImageExtractorFromNormalField< PointT > | Image Extractor which uses the data present in the "normal" field |
| pcl::io::PointCloudImageExtractorFromRGBField< PointT > | Image Extractor which uses the data present in the "rgb" or "rgba" fields to produce a color image with rgb8 encoding |
| pcl::io::PointCloudImageExtractorFromZField< PointT > | Image Extractor which uses the data present in the "z" field to produce a depth map (as a monochrome image with mono16 encoding) |
| pcl::io::PointCloudImageExtractorWithScaling< PointT > | Image Extractor extension which provides functionality to apply scaling to the values extracted from a field |
| pcl::octree::PointCoding< PointT > | PointCoding class |
| pcl::tracking::PointCoherence< PointInT > | PointCoherence is a base class to compute coherence between the two points |
| pcl::PointCorrespondence3D | Representation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g |
| pcl::PointCorrespondence6D | Representation of a (possible) correspondence between two points (e.g |
| pcl::PointDataAtOffset< PointT > | A datatype that enables type-correct comparisons |
| 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< PointT > | PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector |
| pcl::PointRGB | A point structure for representing RGB color |
| pcl::visualization::context_items::Points | |
| 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::PointUV | A 2D point structure representing pixel image coordinates |
| 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 | |
| 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 | 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::Poisson< PointNT > | The Poisson surface reconstruction algorithm |
| pcl::visualization::context_items::Polygon | |
| pcl::geometry::PolygonMesh< MeshTraitsT > | General half-edge mesh that can store any polygon with a minimum number of vertices of 3 |
| pcl::PolygonMesh | |
| pcl::geometry::PolygonMeshTag | Tag describing the type of the mesh |
| pcl::poisson::Polynomial< Degree > | |
| pcl::PolynomialCalculationsT< real > | This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials |
| Eigen::PolynomialSolver< _Scalar, 2 > | |
| pcl::registration::PoseEstimate< PointT > | PoseEstimate struct |
| pcl::PosesFromMatches::PoseEstimate | A result of the pose estimation process |
| pcl::registration::PoseMeasurement< VertexT, InformationT > | PoseMeasurement struct |
| pcl::PosesFromMatches | Calculate 3D transformation based on point correspondencdes |
| pcl::PPFRegistration< PointSource, PointTarget >::PoseWithVotes | Structure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes |
| pcl::PPFEstimation< PointInT, PointNT, PointOutT > | Class that calculates the "surflet" features for each pair in the given pointcloud |
| pcl::PPFHashMapSearch | |
| pcl::PPFRegistration< PointSource, PointTarget > | Class that registers two point clouds based on their sets of PPFSignatures |
| pcl::PPFRGBEstimation< PointInT, PointNT, PointOutT > | |
| pcl::PPFRGBRegionEstimation< PointInT, PointNT, PointOutT > | |
| pcl::PPFRGBSignature | A point structure for storing the Point Pair Color Feature (PPFRGB) values |
| pcl::PPFSignature | A point structure for storing the Point Pair Feature (PPF) values |
| pcl::poisson::PPolynomial< Degree > | |
| pcl::PrincipalCurvatures | A point structure representing the principal curvatures and their magnitudes |
| pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT > | PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals |
| pcl::PrincipalRadiiRSD | A point structure representing the minimum and maximum surface radii (in meters) computed using RSD |
| pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::prioBranchQueueEntry | Priority queue entry for branch nodes |
| pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::prioPointQueueEntry | Priority queue entry for point candidates |
| pcl::ProgressiveSampleConsensus< PointT > | RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Matching with PROSAC – Progressive Sample Consensus", Chum, O |
| pcl::ProjectInliers< PointT > | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |
| pcl::ProjectInliers< pcl::PCLPointCloud2 > | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |
| pcl::PXCGrabber | Grabber for PXC devices |
| pcl::filters::Pyramid< PointT > | Pyramid constructs a multi-scale representation of an organised point cloud |
| pcl::PyramidFeatureHistogram< PointFeature > | Class that compares two sets of features by using a multiscale representation of the features inside a pyramid |
| pcl::geometry::QuadMesh< MeshTraitsT > | Half-edge mesh that can only store quads |
| pcl::geometry::QuadMeshTag | Tag describing the type of the mesh |
| pcl::QuantizableModality | Interface for a quantizable modality |
| pcl::QuantizedMap | |
| pcl::QuantizedMultiModFeature | Feature that defines a position and quantized value in a specific modality |
| pcl::QuantizedNormalLookUpTable | Look-up-table for fast surface normal quantization |
| pcl::RadiusOutlierRemoval< PointT > | RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have |
| pcl::RadiusOutlierRemoval< pcl::PCLPointCloud2 > | RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K |
| pcl::RandomizedMEstimatorSampleConsensus< PointT > | RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus) |
| pcl::RandomizedRandomSampleConsensus< PointT > | RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RAndom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O |
| pcl::RandomSample< PointT > | RandomSample applies a random sampling with uniform probability |
| pcl::RandomSample< pcl::PCLPointCloud2 > | RandomSample applies a random sampling with uniform probability |
| pcl::RandomSampleConsensus< PointT > | RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and
Automated Cartography", Martin A |
| 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::RangeImageSpherical | RangeImageSpherical is derived from the original range image and uses a slightly different spherical projection |
| pcl::visualization::RangeImageVisualizer | Range image visualizer class |
| pcl::visualization::context_items::Rectangle | |
| pcl::ReferenceFrame | |
| pcl::Region3D< PointT > | Region3D represents summary statistics of a 3D collection of points |
| pcl::RegionGrowing< PointT, NormalT > | Implements the well known Region Growing algorithm used for segmentation |
| pcl::RegionGrowingRGB< PointT, NormalT > | Implements the well known Region Growing algorithm used for segmentation based on color of points |
| pcl::RegionXY | Defines a region in XY-space |
| pcl::Registration< PointSource, PointTarget, Scalar > | Registration represents the base registration class for general purpose, ICP-like methods |
| 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 | |
| pcl::RGBPlaneCoefficientComparator< PointT, PointNT > | RGBPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| pcl::tracking::RGBValue | |
| pcl::RIFTEstimation< PointInT, GradientT, PointOutT > | RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity |
| pcl::recognition::RigidTransformSpace | |
| pcl::RobotEyeGrabber | Grabber for the Ocular Robotics RobotEye sensor |
| pcl::poisson::RootInfo | |
| pcl::recognition::RotationSpace | This is a class for a discrete representation of the rotation space based on the axis-angle representation |
| pcl::recognition::RotationSpaceCell | |
| pcl::recognition::RotationSpaceCellCreator | |
| pcl::recognition::RotationSpaceCreator | |
| pcl::RSDEstimation< PointInT, PointNT, PointOutT > | RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves) for a given point cloud dataset containing points and normals |
| pcl::SACSegmentation< PointT > | SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation |
| pcl::SACSegmentationFromNormals< PointT, PointNT > | SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation |
| pcl::SampleConsensus< T > | SampleConsensus represents the base class |
| pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT > | SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al |
| pcl::SampleConsensusModel< PointT > | SampleConsensusModel represents the base model class |
| pcl::SampleConsensusModelCircle2D< PointT > | SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane |
| pcl::SampleConsensusModelCircle3D< PointT > | SampleConsensusModelCircle3D defines a model for 3D circle segmentation |
| 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::SampleConsensusModelFromNormals< PointT, PointNT > | SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation |
| pcl::SampleConsensusModelLine< PointT > | SampleConsensusModelLine defines a model for 3D line segmentation |
| pcl::SampleConsensusModelNormalParallelPlane< PointT, PointNT > | SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints |
| pcl::SampleConsensusModelNormalPlane< PointT, PointNT > | SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints |
| pcl::SampleConsensusModelNormalSphere< PointT, PointNT > | SampleConsensusModelNormalSphere defines a model for 3D sphere segmentation using additional surface normal constraints |
| pcl::SampleConsensusModelParallelLine< PointT > | SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints |
| pcl::SampleConsensusModelParallelPlane< PointT > | SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints |
| pcl::SampleConsensusModelPerpendicularPlane< PointT > | SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints |
| pcl::SampleConsensusModelPlane< PointT > | SampleConsensusModelPlane defines a model for 3D plane segmentation |
| pcl::SampleConsensusModelRegistration< PointT > | SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection |
| pcl::SampleConsensusModelRegistration2D< PointT > | SampleConsensusModelRegistration2D defines a model for Point-To-Point registration outlier rejection using distances between 2D pixels |
| pcl::SampleConsensusModelSphere< PointT > | SampleConsensusModelSphere defines a model for 3D sphere segmentation |
| pcl::SampleConsensusModelStick< PointT > | SampleConsensusModelStick defines a model for 3D stick segmentation |
| pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT > | Pose estimation and alignment class using a prerejective RANSAC routine |
| pcl::SamplingSurfaceNormal< PointT > | SamplingSurfaceNormal divides the input space into grids until each grid contains a maximum of N points, and samples points randomly within each grid |
| pcl::io::ply::ply_parser::scalar_property_callback_type< ScalarType > | |
| pcl::io::ply::ply_parser::scalar_property_definition_callback_type< ScalarType > | |
| pcl::io::ply::ply_parser::scalar_property_definition_callbacks_type | |
| Scene | |
| pcl::ScopeTime | Class to measure the time spent in a scope |
| pcl::keypoints::agast::AbstractAgastDetector::ScoreIndex | Structure holding an index and the associated keypoint score |
| pcl::search::Search< PointT > | Generic search class |
| pcl::SeededHueSegmentation | SeededHueSegmentation |
| 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::recognition::ORROctreeZProjection::Set | |
| 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::ShadowPoints< PointT, NormalT > | ShadowPoints removes the ghost points appearing on edge discontinuties |
| pcl::ShapeContext1980 | A point structure representing a Shape Context |
| pcl::ShapeContext3DEstimation< PointInT, PointNT, PointOutT > | ShapeContext3DEstimation implements the 3D shape context descriptor as described in:
- Andrea Frome, Daniel Huber, Ravi Kolluri and Thomas Bülow, Jitendra Malik Recognizing Objects in Range Data Using Regional Point Descriptors, In proceedings of the 8th European Conference on Computer Vision (ECCV), Prague, May 11-14, 2004
|
| openni_wrapper::OpenNIDevice::ShiftConversion | |
| openni_wrapper::ShiftToDepthConverter | This class provides conversion of the openni 11-bit shift data to depth; |
| pcl::SHOT1344 | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape+color |
| pcl::SHOT352 | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape only |
| pcl::SHOTColorEstimation< PointInT, PointNT, PointOutT, PointRFT > | SHOTColorEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors |
| pcl::SHOTColorEstimationOMP< PointInT, PointNT, PointOutT, PointRFT > | SHOTColorEstimationOMP estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors, in parallel, using the OpenMP standard |
| 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::SHOTEstimationBase< PointInT, PointNT, PointOutT, PointRFT > | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |
| pcl::SHOTEstimationOMP< PointInT, PointNT, PointOutT, PointRFT > | SHOTEstimationOMP estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |
| pcl::SHOTLocalReferenceFrameEstimation< PointInT, PointOutT > | SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor |
| pcl::SHOTLocalReferenceFrameEstimationOMP< PointInT, PointOutT > | SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor |
| pcl::SIFTKeypoint< PointInT, PointOutT > | SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity |
| pcl::SIFTKeypointFieldSelector< PointT > | |
| pcl::SIFTKeypointFieldSelector< PointNormal > | |
| pcl::SIFTKeypointFieldSelector< PointXYZRGB > | |
| pcl::SIFTKeypointFieldSelector< PointXYZRGBA > | |
| pcl::recognition::SimpleOctree< NodeData, NodeDataCreator, Scalar > | |
| pcl::surface::SimplificationRemoveUnusedVertices | |
| pcl::SmoothedSurfacesKeypoint< PointT, PointNT > | Based on the paper: Xinju Li and Igor Guskov Multi-scale features for approximate alignment of point-based surfaces Proceedings of the third Eurographics symposium on Geometry processing July 2005, Vienna, Austria |
| pcl::SolverDidntConvergeException | An exception that is thrown when the non linear solver didn't converge |
| pcl::registration::sortCorrespondencesByDistance | 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::poisson::SortedTreeNodes | |
| pcl::poisson::SparseMatrix< T > | |
| pcl::SparseQuantizedMultiModTemplate | A multi-modality template constructed from a set of quantized multi-modality features |
| pcl::poisson::SparseSymmetricMatrix< T > | |
| pcl::SpinImageEstimation< PointInT, PointNT, PointOutT > | Estimates spin-image descriptors in the given input points |
| pcl::poisson::Square | |
| pcl::poisson::StartingPolynomial< Degree > | |
| pcl::StaticRangeCoder | StaticRangeCoder compression class |
| pcl::StatisticalMultiscaleInterestRegionExtraction< PointT > | Class for extracting interest regions from unstructured point clouds, based on a multi scale statistical approach |
| pcl::StatisticalOutlierRemoval< PointT > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |
| pcl::StatisticalOutlierRemoval< pcl::PCLPointCloud2 > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |
| pcl::StopWatch | Simple stopwatch |
| pcl::Supervoxel< PointT > | Supervoxel container class - stores a cluster extracted using supervoxel clustering |
| pcl::SupervoxelClustering< PointT > | Implements a supervoxel algorithm based on voxel structure, normals, and rgb values |
| pcl::SurfaceNormalModality< PointInT > | Modality based on surface normals |
| pcl::SurfaceReconstruction< PointInT > | SurfaceReconstruction represents a base surface reconstruction class |
| pcl::SurfelSmoothing< PointT, PointNT > | |
| pcl::SUSANKeypoint< PointInT, PointOutT, NormalT, IntensityT > | SUSANKeypoint implements a RGB-D extension of the SUSAN detector inluding normal directions variation in top of intensity variation |
| pcl::SynchronizedQueue< T > | |
| 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::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TC | This structure is used for determining the end of the k-means clustering process |
| pcl::TexMaterial | |
| pcl::visualization::context_items::Text | |
| pcl::TextureMapping< PointInT > | The texture mapping algorithm |
| pcl::TextureMesh | |
| pcl::TfQuadraticXYZComparison< PointT > | A comparison whether the (x,y,z) components of a given point satisfy (p'Ap + 2v'p + c [OP] 0) |
| pcl::console::TicToc | |
| pcl::TimeTrigger | Timer class that invokes registered callback methods periodically |
| pcl::tracking::Tracker< PointInT, StateT > | Tracker represents the base tracker class |
| pcl::registration::TransformationEstimation< PointSource, PointTarget, Scalar > | TransformationEstimation represents the base class for methods for transformation estimation based on:
- correspondence vectors
- two point clouds (source and target) of the same size
- a point cloud with a set of indices (source), and another point cloud (target)
- two point clouds with two sets of indices (source and target) of the same size
|
| pcl::registration::TransformationEstimation2D< PointSource, PointTarget, Scalar > | TransformationEstimation2D implements a simple 2D rigid transformation estimation (x, y, theta) for a given pair of datasets |
| pcl::registration::TransformationEstimationDQ< PointSource, PointTarget, Scalar > | TransformationEstimationDQ implements dual quaternion based estimation of the transformation aligning the given correspondences |
| pcl::registration::TransformationEstimationDualQuaternion< PointSource, PointTarget, Scalar > | TransformationEstimationDualQuaternion implements dual quaternion based estimation of the transformation aligning the given correspondences |
| pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar > | TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences |
| pcl::registration::TransformationEstimationPointToPlane< PointSource, PointTarget, Scalar > | 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, Scalar > | 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::TransformationEstimationPointToPlaneLLSWeighted< PointSource, PointTarget, Scalar > | TransformationEstimationPointToPlaneLLSWeighted implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals, with the possibility of assigning weights to the correspondences |
| pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar > | TransformationEstimationPointToPlaneWeighted uses Levenberg Marquardt optimization to find the transformation that minimizes the point-to-plane distance between the given correspondences |
| pcl::registration::TransformationEstimationSVD< PointSource, PointTarget, Scalar > | TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences |
| pcl::registration::TransformationEstimationSVDScale< PointSource, PointTarget, Scalar > | 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, Scalar > | TransformationValidation represents the base class for methods that validate the correctness of a transformation found through TransformationEstimation |
| pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget, Scalar > | TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset |
| pcl::poisson::TreeNodeData | |
| pcl::poisson::Triangle | |
| pcl::poisson::TriangleIndex | |
| pcl::geometry::TriangleMesh< MeshTraitsT > | Half-edge mesh that can only store triangles |
| pcl::geometry::TriangleMeshTag | Tag describing the type of the mesh |
| pcl::poisson::Triangulation< Real > | |
| pcl::poisson::TriangulationEdge | |
| pcl::poisson::TriangulationTriangle | |
| pcl::recognition::TrimmedICP< PointT, Scalar > | |
| pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::TruncatedError | |
| pcl::UnhandledPointTypeException | |
| pcl::common::uniform_distribution< float > | Uniform distribution float specialized |
| pcl::common::uniform_distribution< int > | Uniform distribution int specialized |
| pcl::common::UniformGenerator< T > | UniformGenerator class generates a random number from range [min, max] at each run picked according to a uniform distribution i.e eaach number within [min, max] has almost the same probability of being drawn |
| pcl::UniformSampling< PointInT > | UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT > | UniqueShapeContext implements the Unique Shape Context Descriptor described here: |
| pcl::UnorganizedPointCloudException | An exception that is thrown when an organized point cloud is needed but not provided |
| pcl::poisson::UpSampleData | |
| pcl::texture_mapping::UvIndex | Structure that links a uv coordinate to its 3D point and face |
| pcl::ndt2d::ValueAndDerivatives< N, T > | Class to store vector value and first and second derivatives (grad vector and hessian matrix), so they can be returned easily from functions |
| pcl::poisson::Vector< T > | |
| pcl::VectorAverage< real, dimension > | Calculates the weighted average and the covariance matrix |
| pcl::registration::ELCH< PointT >::Vertex | |
| pcl::poisson::CoredMeshData2::Vertex | |
| pcl::geometry::Vertex | A vertex is a node in the mesh |
| pcl::geometry::VertexAroundFaceCirculator< MeshT > | Circulates clockwise around a face and returns an index to the terminating vertex of the inner half-edge (the target) |
| pcl::geometry::VertexAroundVertexCirculator< MeshT > | Circulates counter-clockwise around a vertex and returns an index to the terminating vertex of the outgoing half-edge (the target) |
| pcl::poisson::VertexData | |
| pcl::geometry::VertexIndex | Index used to access elements in the half-edge mesh |
| pcl::registration::LUM< PointT >::VertexProperties | |
| pcl::Vertices | Describes a set of vertices in a polygon mesh, by basically storing an array of indices |
| pcl::VFHEstimation< PointInT, PointNT, PointOutT > | 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) |
| Viewport | |
| pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::VisualWordStat | Structure for storing the visual word |
| pcl::SupervoxelClustering< PointT >::VoxelData | VoxelData is a structure used for storing data within a pcl::octree::OctreePointCloudAdjacencyContainer |
| pcl::VoxelGrid< PointT > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| pcl::VoxelGrid< pcl::PCLPointCloud2 > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| pcl::VoxelGridCovariance< PointT > | A searchable voxel strucure containing the mean and covariance of the data |
| pcl::VoxelGridLabel | |
| pcl::VoxelGridOcclusionEstimation< PointT > | VoxelGrid to estimate occluded space in the scene |
| pcl::recognition::VoxelStructure< T, REAL > | This class is a box in R3 built of voxels ordered in a regular rectangular grid |
| pcl::VTKUtils | |
| vtkVertexBufferObject | |
| vtkVertexBufferObjectMapper | |
| pcl::registration::WarpPointRigid< PointSourceT, PointTargetT, Scalar > | Base warp point class |
| pcl::registration::WarpPointRigid3D< PointSourceT, PointTargetT, Scalar > | WarpPointRigid3D enables 3D (1D rotation + 2D translation) transformations for points |
| pcl::registration::WarpPointRigid6D< PointSourceT, PointTargetT, Scalar > | WarpPointRigid3D enables 6D (3D rotation + 3D translation) transformations for points |
| 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 |
| pcl::occlusion_reasoning::ZBuffering< ModelT, SceneT > | Class to reason about occlusions |
