Here are the classes, structs, unions and interfaces with brief descriptions:

pcl::_PointWithViewpoint | |

pcl::_PointXYZ | |

pcl::AdaptiveRangeCoder | AdaptiveRangeCoder compression class |

pcl::traits::asEnum< T > | |

pcl::traits::asEnum< double > | |

pcl::traits::asEnum< float > | |

pcl::traits::asEnum< int16_t > | |

pcl::traits::asEnum< int32_t > | |

pcl::traits::asEnum< int8_t > | |

pcl::traits::asEnum< uint16_t > | |

pcl::traits::asEnum< uint32_t > | |

pcl::traits::asEnum< uint8_t > | |

pcl::traits::asType< int > | |

pcl::traits::asType< sensor_msgs::PointField::FLOAT32 > | |

pcl::traits::asType< sensor_msgs::PointField::FLOAT64 > | |

pcl::traits::asType< sensor_msgs::PointField::INT16 > | |

pcl::traits::asType< sensor_msgs::PointField::INT32 > | |

pcl::traits::asType< sensor_msgs::PointField::INT8 > | |

pcl::traits::asType< sensor_msgs::PointField::UINT16 > | |

pcl::traits::asType< sensor_msgs::PointField::UINT32 > | |

pcl::traits::asType< sensor_msgs::PointField::UINT8 > | |

pcl::BivariatePolynomialT< real > | This represents a bivariate polynomial and provides some functionality for it |

pcl::BorderDescription | A structure to store if a point in a range image lies on a border between an obstacle and the background |

pcl::Boundary | A point structure representing a description of whether a point is lying on a surface boundary or not |

pcl::BoundaryEstimation< PointInT, PointNT, PointOutT > | BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion |

pcl::visualization::Camera | Camera class holds a set of camera parameters together with the window pos/size |

pcl::visualization::CloudActor | |

pcl::visualization::CloudViewer | Simple point cloud visualization class |

pcl::octree::ColorCoding< PointT > | ColorCoding class |

pcl::ComparisonBase< PointT > | The (abstract) base class for the comparison object |

pcl::ConcaveHull< PointInT > | ConcaveHull (alpha shapes) using libqhull library |

pcl::ConditionalRemoval< PointT > | ConditionalRemoval filters data that satisfies certain conditions |

pcl::ConditionAnd< PointT > | AND condition |

pcl::ConditionBase< PointT > | Base condition class |

pcl::ConditionOr< PointT > | OR condition |

pcl::octree::configurationProfile_t | |

pcl::ConvexHull< PointInT > | ConvexHull using libqhull library |

pcl::registration::Correspondence | Class representing a match between two descriptors |

pcl::registration::CorrespondenceEstimation< PointSource, PointTarget > | CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features |

pcl::registration::CorrespondenceRejector | CorrespondenceRejector represents the base class for correspondence rejection methods |

pcl::registration::CorrespondenceRejectorDistance | CorrespondenceRejectorDistance implements a simple correspondence rejection method based on thresholding the distances between the correspondences |

pcl::registration::CorrespondenceRejectorOneToOne | CorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences |

pcl::registration::CorrespondenceRejectorReciprocal | CorrespondenceRejectorReciprocal implements a reciprocal correspondence rejection method for ICP-like registration algorithms |

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::CVFHEstimation< PointInT, PointNT, PointOutT > | CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing points and normals |

pcl::traits::datatype< PointT, Tag > | |

pcl::traits::decomposeArray< T > | |

pcl::DefaultPointRepresentation< PointDefault > | DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types |

pcl::DefaultPointRepresentation< FPFHSignature33 > | |

pcl::DefaultPointRepresentation< PFHSignature125 > | |

pcl::DefaultPointRepresentation< PointNormal > | |

pcl::DefaultPointRepresentation< PointXYZ > | |

pcl::DefaultPointRepresentation< PointXYZI > | |

openni_wrapper::DepthImage | This class provides methods to fill a depth or disparity image |

openni_wrapper::OpenNIDriver::DeviceContext | |

openni_wrapper::DeviceKinect | Concrete implementation of the interface OpenNIDevice for a MS Kinect device |

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::EuclideanClusterExtraction< PointT > | EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense |

pcl::ExtractIndices< PointT > | ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud |

pcl::ExtractIndices< sensor_msgs::PointCloud2 > | ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud |

pcl::ExtractPolygonalPrismData< PointT > | ExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given height, generates a 3D polygonal prism |

pcl::Feature< PointInT, PointOutT > | Feature represents the base feature class |

pcl::FeatureFromNormals< PointInT, PointNT, PointOutT > | |

pcl::Narf::FeaturePointRepresentation | |

pcl::detail::FieldAdder< PointT > | |

pcl::FieldComparison< PointT > | The field-based specialization of the comparison object |

pcl::traits::fieldList< PointT > | |

pcl::detail::FieldMapper< PointT > | |

pcl::detail::FieldMapping | |

pcl::Filter< PointT > | Filter represents the base filter class |

pcl::Filter< sensor_msgs::PointCloud2 > | Filter represents the base filter class |

pcl::for_each_type_impl< done > | |

pcl::for_each_type_impl< false > | |

pcl::FPFHEstimation< PointInT, PointNT, PointOutT > | FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals |

pcl::FPFHEstimationOMP< PointInT, PointNT, PointOutT > | FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |

pcl::FPFHSignature33 | A point structure representing the Fast Point Feature Histogram (FPFH) |

pcl::visualization::FPSCallback | |

pcl::Grabber | |

pcl::GreedyProjectionTriangulation< PointInT > | GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections |

pcl::GridProjection< PointNT > | Grid projection surface reconstruction method |

std_msgs::Header | |

pcl::Histogram< N > | A point structure representing an N-D histogram |

openni_wrapper::Image | Image class containing just a reference to image meta data |

sensor_msgs::Image | |

openni_wrapper::ImageBayerGRBG | This class provides methods to fill a RGB or Grayscale image buffer from underlying Bayer pattern image |

openni_wrapper::ImageYUV422 | Concrete implementation of the interface Image for a YUV 422 image used by Primesense devices |

pcl::IntegralImage2D< DataType, IIDataType > | Generic implementation for creating 2D integral images (including second order integral images) |

pcl::IntegralImageNormalEstimation< PointInT, PointOutT > | Surface normal estimation on dense data using integral images |

pcl::IntensityGradient | A point structure representing the intensity gradient of an XYZI point cloud |

pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT > | IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values |

pcl::IntensitySpinEstimation< PointInT, PointOutT > | IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity |

pcl::InterestPoint | A point structure representing an interest point with Euclidean xyz coordinates, and an interest value |

pcl::InvalidConversionException | An exception that is thrown when a PointCloud2 message cannot be converted into a PCL type |

pcl::PosesFromMatches::PoseEstimate::IsBetter | |

pcl::IsNotDenseException | An exception that is thrown when a PointCloud is not dense but is attemped to be used as dense |

pcl::IterativeClosestPoint< PointSource, PointTarget > | IterativeClosestPoint is an implementation of the Iterative Closest Point algorithm based on Singular Value Decomposition (SVD) |

pcl::IterativeClosestPointNonLinear< PointSource, PointTarget > | IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend |

pcl::KdTree< PointT > | KdTree represents the base spatial locator class for nearest neighbor estimation |

pcl::KdTreeFLANN< PointT > | KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures |

pcl::Keypoint< PointInT, PointOutT > | Keypoint represents the base class for key points |

pcl::GridProjection< PointNT >::Leaf | Data leaf |

pcl::VoxelGrid< PointT >::Leaf | Simple structure to hold an nD centroid and the number of points in a leaf |

pcl::VoxelGrid< sensor_msgs::PointCloud2 >::Leaf | Simple structure to hold an nD centroid and the number of points in a leaf |

pcl::LeastMedianSquares< PointT > | LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm |

pcl::RangeImageBorderExtractor::LocalSurface | Stores some information extracted from the neighborhood of a point |

pcl::MaximumLikelihoodSampleConsensus< PointT > | MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to
estimating image geometry", P.H.S |

pcl::MEstimatorSampleConsensus< PointT > | MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S |

pcl::ModelCoefficients | |

pcl::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 |

pcl::MovingLeastSquares< PointInT, NormalOutT > | MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation |

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 |

pcl::NarfKeypoint | NARF (Normal Aligned Radial Feature) keypoints |

pcl::NdCentroidFunctor< PointT > | Helper functor structure for n-D centroid estimation |

pcl::NdConcatenateFunctor< PointInT, PointOutT > | Helper functor structure for concatenate |

pcl::NdCopyEigenPointFunctor< PointT > | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |

pcl::NdCopyPointEigenFunctor< PointT > | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |

pcl::OrganizedNeighborSearch< PointT >::nearestNeighborCandidate | nearestNeighborCandidate entry for the nearest neighbor candidate queue |

pcl::Normal | A point structure representing normal coordinates and the surface curvature estimate |

pcl::NormalEstimation< PointInT, PointOutT > | NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures |

pcl::NormalEstimationOMP< PointInT, PointOutT > | NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard |

pcl::octree::Octree2BufBase< DataT, LeafT > | Octree double buffer class |

pcl::octree::OctreeBase< DataT, LeafT > | Octree class |

pcl::octree::Octree2BufBase< DataT, LeafT >::OctreeBranch | Octree branch class |

pcl::octree::OctreeBase< DataT, LeafT >::OctreeBranch | Octree branch class |

pcl::octree::OctreeLowMemBase< DataT, LeafT >::OctreeBranch | Octree branch class |

pcl::octree::Octree2BufBase< DataT, LeafT >::OctreeKey | Octree key class |

pcl::octree::OctreeBase< DataT, LeafT >::OctreeKey | Octree key class |

pcl::octree::OctreeLowMemBase< DataT, LeafT >::OctreeKey | Octree key class |

pcl::octree::OctreeLeafAbstract< DataT > | Abstract octree leaf class |

pcl::octree::OctreeLeafDataT< DataT > | Octree leaf class that does store a single DataT element |

pcl::octree::OctreeLeafDataTVector< DataT > | Octree leaf class that does store a vector of DataT elements |

pcl::octree::OctreeLeafEmpty< DataT > | Octree leaf class that does not store any information |

pcl::octree::OctreeLowMemBase< DataT, LeafT > | Octree class |

pcl::octree::OctreeNode | Abstract octree node class |

pcl::octree::OctreePointCloud< PointT, LeafT, OctreeT > | Octree pointcloud class |

pcl::octree::OctreePointCloudChangeDetector< PointT, LeafT > | Octree pointcloud change detector class |

pcl::octree::OctreePointCloudDensity< PointT, LeafT, OctreeT > | Octree pointcloud density class |

pcl::octree::OctreePointCloudDensityLeaf< DataT > | Octree pointcloud density leaf node class |

pcl::octree::OctreePointCloudOccupancy< PointT, LeafT, OctreeT > | Octree pointcloud occupancy class |

pcl::octree::OctreePointCloudPointVector< PointT, LeafT, OctreeT > | Octree pointcloud point vector class |

pcl::octree::OctreePointCloudSinglePoint< PointT, LeafT, OctreeT > | Octree pointcloud single point class |

pcl::octree::OctreePointCloudVoxelCentroid< PointT, LeafT, OctreeT > | Octree pointcloud voxel centroid class |

pcl::traits::offset< PointT, Tag > | |

openni_wrapper::OpenNIDevice | Class representing an astract device for Primesense or MS Kinect devices |

openni_wrapper::OpenNIDriver | Driver class implemented as Singleton |

openni_wrapper::OpenNIException | General exception class |

pcl::OpenNIGrabber | /brief /ingroup io |

pcl::OrganizedDataIndex< PointT > | OrganizedDataIndex is a type of spatial locator used to query organized datasets, such as point clouds acquired using dense stereo devices |

pcl::OrganizedNeighborSearch< PointT > | OrganizedNeighborSearch class |

pcl::PackedHSIComparison< PointT > | A packed HSI specialization of the comparison object |

pcl::PackedRGBComparison< PointT > | A packed rgb specialization of the comparison object |

pcl::NarfDescriptor::Parameters | |

pcl::RangeImageBorderExtractor::Parameters | Parameters used in this class |

pcl::PolynomialCalculationsT< real >::Parameters | Parameters used in this class |

pcl::NarfKeypoint::Parameters | Parameters used in this class |

pcl::PosesFromMatches::Parameters | Parameters used in this class |

pcl::PassThrough< PointT > | PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints |

pcl::PassThrough< sensor_msgs::PointCloud2 > | PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints |

pcl::PCA< PointT > | Principal Component analysis (PCA) class |

pcl::PCDGrabber< PointT > | |

pcl::PCDGrabberBase | Base class for PCD file grabber |

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< sensor_msgs::PointCloud2 > | |

pcl::PCLException | A base class for all pcl exceptions which inherits from std::runtime_error |

pcl::visualization::PCLHistogramVisualizer | PCL histogram visualizer main class |

pcl::visualization::PCLHistogramVisualizerInteractorStyle | PCL histogram visualizer interactory style class |

pcl::PCLIOException | /brief /ingroup io |

pcl::visualization::PCLVisualizer | PCL Visualizer main class |

pcl::visualization::PCLVisualizerInteractor | The PCLVisualizer interactor |

pcl::visualization::PCLVisualizerInteractorStyle | PCL Visualizer interactory style class |

pcl::PFHEstimation< PointInT, PointNT, PointOutT > | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |

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::traits::POD< PointT > | |

pcl::PointCloud< PointT > | PointCloud represents a templated PointCloud implementation |

sensor_msgs::PointCloud2 | |

pcl::visualization::PointCloudColorHandler< PointT > | Base Handler class for PointCloud colors |

pcl::visualization::PointCloudColorHandler< sensor_msgs::PointCloud2 > | Base Handler class for PointCloud colors |

pcl::visualization::PointCloudColorHandlerCustom< PointT > | Handler for predefined user colors |

pcl::visualization::PointCloudColorHandlerCustom< sensor_msgs::PointCloud2 > | Handler for predefined user colors |

pcl::visualization::PointCloudColorHandlerGenericField< PointT > | Generic field handler class for colors |

pcl::visualization::PointCloudColorHandlerGenericField< sensor_msgs::PointCloud2 > | Generic field handler class for colors |

pcl::visualization::PointCloudColorHandlerRandom< PointT > | Handler for random PointCloud colors |

pcl::visualization::PointCloudColorHandlerRandom< sensor_msgs::PointCloud2 > | Handler for random PointCloud colors |

pcl::visualization::PointCloudColorHandlerRGBField< PointT > | RGB handler class for colors |

pcl::visualization::PointCloudColorHandlerRGBField< sensor_msgs::PointCloud2 > | RGB handler class for colors |

pcl::octree::PointCloudCompression< PointT, LeafT, OctreeT > | Octree pointcloud compression class |

pcl::visualization::PointCloudGeometryHandler< PointT > | Base handler class for PointCloud geometry |

pcl::visualization::PointCloudGeometryHandler< sensor_msgs::PointCloud2 > | Base handler class for PointCloud geometry |

pcl::visualization::PointCloudGeometryHandlerCustom< PointT > | Custom handler class for PointCloud geometry |

pcl::visualization::PointCloudGeometryHandlerCustom< sensor_msgs::PointCloud2 > | Custom handler class for PointCloud geometry |

pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< PointT > | Surface normal handler class for PointCloud geometry |

pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< sensor_msgs::PointCloud2 > | Surface normal handler class for PointCloud geometry |

pcl::visualization::PointCloudGeometryHandlerXYZ< PointT > | XYZ handler class for PointCloud geometry |

pcl::visualization::PointCloudGeometryHandlerXYZ< sensor_msgs::PointCloud2 > | XYZ handler class for PointCloud geometry |

pcl::octree::PointCoding< PointT > | PointCoding class |

pcl::PointCorrespondence | Representation of a (possible) correspondence between two points in two different coordinate frames (e.g |

pcl::PointCorrespondence3D | Representation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g |

pcl::PointCorrespondence6D | Representation of a (possible) correspondence between two points (e.g |

pcl::PointDataAtOffset< PointT > | A datatype that enables type-correct comparisons |

sensor_msgs::PointField | |

pcl::PointIndices | |

pcl::PointNormal | A point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate |

pcl::PointRepresentation< PointT > | PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector |

pcl::PointSurfel | A surfel, that is, a point structure representing Euclidean xyz coordinates, together with normal coordinates, a RGBA color, a radius, a confidence value and the surface curvature estimate |

pcl::PointWithRange | A point structure representing Euclidean xyz coordinates, padded with an extra range float |

pcl::PointWithScale | A point structure representing a 3-D position and scale |

pcl::PointWithViewpoint | A point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen |

pcl::PointXY | A 2D point structure representing Euclidean xy coordinates |

pcl::PointXYZ | A point structure representing Euclidean xyz coordinates |

pcl::PointXYZI | A point structure representing Euclidean xyz coordinates, and the intensity value |

pcl::PointXYZINormal | A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate |

pcl::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::PointXYZRGBNormal | A point structure representing Euclidean xyz coordinates, and the RGB color, together with normal coordinates and the surface curvature estimate |

pcl::PolygonMesh | |

pcl::PolynomialCalculationsT< real > | This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials |

pcl::PosesFromMatches::PoseEstimate | A result of the pose estimation process |

pcl::PosesFromMatches | Calculate 3D transformation based on point correspondencdes |

pcl::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::OctreePointCloud< PointT, LeafT, OctreeT >::prioBranchQueueEntry | Priority queue entry for branch nodes |

pcl::octree::OctreePointCloud< PointT, LeafT, OctreeT >::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< sensor_msgs::PointCloud2 > | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |

pcl::RadiusOutlierRemoval< PointT > | RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K |

pcl::RadiusOutlierRemoval< sensor_msgs::PointCloud2 > | RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K |

pcl::OrganizedNeighborSearch< PointT >::radiusSearchLoopkupEntry | radiusSearchLoopkupEntry entry for radius search lookup vector |

pcl::RandomizedMEstimatorSampleConsensus< PointT > | RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus) |

pcl::RandomizedRandomSampleConsensus< PointT > | RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RAndom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O |

pcl::RandomSampleConsensus< PointT > | RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and
Automated Cartography", Martin A |

pcl::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::Registration< PointSource, PointTarget > | Registration represents the base registration class |

pcl::visualization::RenWinInteract | |

pcl::RIFTEstimation< PointInT, GradientT, PointOutT > | RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity |

pcl::RSDEstimation< PointInT, PointNT, PointOutT > | RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves) for a given point cloud dataset containing points and normals |

pcl::SACSegmentation< PointT > | SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation |

pcl::SACSegmentationFromNormals< PointT, PointNT > | SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation |

pcl::SampleConsensus< T > | SampleConsensus represents the base class |

pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT > | SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al |

pcl::SampleConsensusModel< PointT > | SampleConsensusModel represents the base model class |

pcl::SampleConsensusModelCircle2D< PointT > | SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane |

pcl::SampleConsensusModelCylinder< PointT, PointNT > | SampleConsensusModelCylinder defines a model for 3D cylinder segmentation |

pcl::SampleConsensusModelFromNormals< PointT, PointNT > | SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation |

pcl::SampleConsensusModelLine< PointT > | SampleConsensusModelLine defines a model for 3D line segmentation |

pcl::SampleConsensusModelNormalParallelPlane< PointT, PointNT > | SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints |

pcl::SampleConsensusModelNormalPlane< PointT, PointNT > | SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints |

pcl::SampleConsensusModelParallelLine< PointT > | SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints |

pcl::SampleConsensusModelParallelPlane< PointT > | SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints |

pcl::SampleConsensusModelPerpendicularPlane< PointT > | SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints |

pcl::SampleConsensusModelPlane< PointT > | SampleConsensusModelPlane defines a model for 3D plane segmentation |

pcl::SampleConsensusModelRegistration< PointT > | SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection |

pcl::SampleConsensusModelSphere< PointT > | SampleConsensusModelSphere defines a model for 3D sphere segmentation |

pcl::ScopeTime | Class to measure the time spent in a scope |

pcl::SegmentDifferences< PointT > | SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold |

pcl::RangeImageBorderExtractor::ShadowBorderIndices | Stores the indices of the shadow border corresponding to obstacle borders |

pcl::SIFTKeypoint< PointInT, PointOutT > | SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity |

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::StaticRangeCoder | StaticRangeCoder compression class |

pcl::StatisticalOutlierRemoval< PointT > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |

pcl::StatisticalOutlierRemoval< sensor_msgs::PointCloud2 > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |

pcl::SurfaceReconstruction< PointInT > | SurfaceReconstruction represents the base surface reconstruction class |

pcl::Synchronizer< T1, T2 > | /brief /ingroup io |

pcl::console::TicToc | |

pcl::TimeTrigger | Timer class that invokes registered callback methods periodically |

pcl::registration::TransformationEstimation< PointSource, PointTarget > | TransformationEstimation represents the base class for methods for transformation estimation based on given a correspondence vector |

pcl::registration::TransformationEstimationSVD< PointSource, PointTarget > | TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences in target and input point cloud |

pcl::TransformationFromCorrespondences | Calculates a transformation based on corresponding 3D points |

pcl::VectorAverage< real, dimension > | Calculates the weighted average and the covariance matrix |

pcl::Vertices | Describes a set of vertices in a polygon mesh, by basically storing an array of indices |

pcl::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) |

pcl::VoxelGrid< PointT > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |

pcl::VoxelGrid< sensor_msgs::PointCloud2 > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |