  flann | |
   NNIndex | |
| L2 | |
| L2_Simple | |
 Matrix | |
| openni_wrapper | |
| IRImage | Class containing just a reference to IR meta data |
| pcl | |
| common | |
| IntensityFieldAccessor< pcl::PointNormal > | |
| IntensityFieldAccessor< pcl::PointXYZRGB > | |
| IntensityFieldAccessor< pcl::PointXYZRGBA > | |
| IntensityFieldAccessor | |
| ComparisonOps | |
| console | |
| TicToc | |
| detail | |
| FieldMapping | |
| FieldAdder | |
| FieldMapper | |
| distances | |
| fields | |
| geometry | |
| io | |
| ply | |
| io_operators | |
| type_traits | |
 ply_parser | Class ply_parser parses a PLY file and generates appropriate atomic parsers for the body |
 list_property_begin_callback_type | |
| list_property_definition_callback_type | |
| list_property_definition_callbacks_type | |
| list_property_element_callback_type | |
| list_property_end_callback_type | |
| scalar_property_callback_type | |
| scalar_property_definition_callback_type | |
| scalar_property_definition_callbacks_type | |
| TARHeader | A TAR file's header, as described on http://en.wikipedia.org/wiki/Tar_%28file_format%29 |
| octree | |
| ColorCoding | ColorCoding class |
| configurationProfile_t | |
| PointCloudCompression | Octree pointcloud compression class |
| PointCoding | PointCoding class |
| BufferedBranchNode | |
| Octree2BufBase | Octree double buffer class |
| OctreeBase | Octree class |
| OctreeContainerEmpty | Octree leaf class that does not store any information |
| OctreeContainerDataT | Octree leaf class that does store a single DataT element |
| OctreeContainerDataTVector | Octree leaf class that does store a vector of DataT elements |
| OctreeIteratorBase | Abstract octree iterator class |
| OctreeDepthFirstIterator | Octree iterator class |
| OctreeBreadthFirstIterator | Octree iterator class |
| OctreeLeafNodeIterator | Octree leaf node iterator class |
| OctreeKey | Octree key class |
| OctreeNodePool | Octree node pool |
| OctreeNode | Abstract octree node class |
| OctreeLeafNode | Abstract octree leaf class |
| OctreeBranchNode | Abstract octree branch class |
| OctreePointCloud | Octree pointcloud class |
| OctreePointCloudChangeDetector | Octree pointcloud change detector class |
| OctreePointCloudDensityContainer | Octree pointcloud density leaf node class |
| OctreePointCloudDensity | Octree pointcloud density class |
| OctreePointCloudOccupancy | Octree pointcloud occupancy class |
| OctreePointCloudPointVector | Octree pointcloud point vector class |
| OctreePointCloudSinglePoint | Octree pointcloud single point class |
| OctreePointCloudVoxelCentroid | Octree pointcloud voxel centroid class |
| OctreePointCloudSearch | Octree pointcloud search class |
| registration | |
| CorrespondenceEstimation | CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features |
| CorrespondenceEstimationNormalShooting | CorrespondenceEstimationNormalShooting computes correspondences as points in the target cloud which have minimum distance to normals computed on the input cloud |
| CorrespondenceRejector | CorrespondenceRejector represents the base class for correspondence rejection methods |
| DataContainerInterface | DataContainerInterface provides a generic interface for computing correspondence scores between correspondent points in the input and target clouds |
| DataContainer | 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 |
| CorrespondenceRejectorDistance | CorrespondenceRejectorDistance implements a simple correspondence rejection method based on thresholding the distances between the correspondences |
| CorrespondenceRejectorFeatures | CorrespondenceRejectorFeatures implements a correspondence rejection method based on a set of feature descriptors |
| CorrespondenceRejectorMedianDistance | CorrespondenceRejectorMedianDistance implements a simple correspondence rejection method based on thresholding based on the median distance between the correspondences |
| CorrespondenceRejectorOneToOne | CorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences |
| CorrespondenceRejectorSampleConsensus | CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers) |
| CorrespondenceRejectorSurfaceNormal | CorrespondenceRejectorSurfaceNormal implements a simple correspondence rejection method based on the angle between the normals at correspondent points |
| 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 |
| CorrespondenceRejectorVarTrimmed | CorrespondenceRejectoVarTrimmed implements a simple correspondence rejection method by considering as inliers a certain percentage of correspondences with the least distances |
| sortCorrespondencesByQueryIndex | sortCorrespondencesByQueryIndex : a functor for sorting correspondences by query index |
| sortCorrespondencesByMatchIndex | sortCorrespondencesByMatchIndex : a functor for sorting correspondences by match index |
| sortCorrespondencesByDistance | sortCorrespondencesByDistance : a functor for sorting correspondences by distance |
| sortCorrespondencesByQueryIndexAndDistance | sortCorrespondencesByQueryIndexAndDistance : a functor for sorting correspondences by query index and distance |
| sortCorrespondencesByMatchIndexAndDistance | sortCorrespondencesByMatchIndexAndDistance : a functor for sorting correspondences by match index and distance |
| ELCH | ELCH (Explicit Loop Closing Heuristic) class |
| Vertex | |
| TransformationEstimation | TransformationEstimation represents the base class for methods for transformation estimation based on: |
| TransformationEstimationLM | TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences |
| TransformationEstimationPointToPlane | TransformationEstimationPointToPlane uses Levenberg Marquardt optimization to find the transformation that minimizes the point-to-plane distance between the given correspondences |
| TransformationEstimationPointToPlaneLLS | TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals |
| TransformationEstimationSVD | TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences |
| TransformationValidation | TransformationValidation represents the base class for methods that validate the correctness of a transformation found through TransformationEstimation |
| TransformationValidationEuclidean | TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset |
| search | |
| BruteForce | Implementation of a simple brute force search algorithm |
| FlannSearch | search::FlannSearch is a generic FLANN wrapper class for the new search interface |
| FlannIndexCreator | Helper class that creates a FLANN index from a given FLANN matrix |
| KdTreeIndexCreator | Creates a FLANN KdTreeSingleIndex from the given input data |
| KdTree | search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure |
| Octree | search::Octree is a wrapper class which implements nearest neighbor search operations based on the pcl::octree::Octree structure |
| OrganizedNeighbor | OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds |
| Search | Generic search class |
| surface | |
| SimplificationRemoveUnusedVertices | |
| test | Test_macros.h provide helper macros for testing vectors, matrices etc |
| texture_mapping | |
| Camera | Structure to store camera pose and focal length |
| UvIndex | Structure that links a uv coordinate to its 3D point and face |
| traits | |
| asEnum | |
| asEnum< int8_t > | |
| asEnum< uint8_t > | |
| asEnum< int16_t > | |
| asEnum< uint16_t > | |
| asEnum< int32_t > | |
| asEnum< uint32_t > | |
| asEnum< float > | |
| asEnum< double > | |
| asType | |
| asType< sensor_msgs::PointField::INT8 > | |
| asType< sensor_msgs::PointField::UINT8 > | |
| asType< sensor_msgs::PointField::INT16 > | |
| asType< sensor_msgs::PointField::UINT16 > | |
| asType< sensor_msgs::PointField::INT32 > | |
| asType< sensor_msgs::PointField::UINT32 > | |
| asType< sensor_msgs::PointField::FLOAT32 > | |
| asType< sensor_msgs::PointField::FLOAT64 > | |
| decomposeArray | |
| POD | |
| name | |
| offset | |
| datatype | |
| fieldList | |
| utils | |
| visualization | |
| CloudViewer | Simple point cloud visualization class |
| CloudActor | |
| Camera | Camera class holds a set of camera parameters together with the window pos/size |
| FPSCallback | |
| FloatImageUtils | Provide some gerneral functionalities regarding 2d float arrays, e.g., for visualization purposes |
| RenWinInteract | |
| PCLHistogramVisualizer | PCL histogram visualizer main class |
| ImageViewer | ImageViewer is a class for 2D image visualization |
| PCLVisualizerInteractor | The PCLVisualizer interactor |
| PCLVisualizerInteractorStyle | PCLVisualizerInteractorStyle defines an unique, custom VTK based interactory style for PCL Visualizer applications |
| PCLHistogramVisualizerInteractorStyle | PCL histogram visualizer interactory style class |
| KeyboardEvent | /brief Class representing key hit/release events |
| MouseEvent | |
| PCLVisualizer | PCL Visualizer main class |
| PointCloudGeometryHandler | Base handler class for PointCloud geometry |
| PointCloudGeometryHandlerXYZ | XYZ handler class for PointCloud geometry |
| PointCloudGeometryHandlerSurfaceNormal | Surface normal handler class for PointCloud geometry |
| PointCloudGeometryHandlerCustom | Custom handler class for PointCloud geometry |
| PointCloudGeometryHandler< sensor_msgs::PointCloud2 > | Base handler class for PointCloud geometry |
| PointCloudGeometryHandlerXYZ< sensor_msgs::PointCloud2 > | XYZ handler class for PointCloud geometry |
| PointCloudGeometryHandlerSurfaceNormal< sensor_msgs::PointCloud2 > | Surface normal handler class for PointCloud geometry |
| PointCloudGeometryHandlerCustom< sensor_msgs::PointCloud2 > | Custom handler class for PointCloud geometry |
| PointCloudColorHandler | Base Handler class for PointCloud colors |
| PointCloudColorHandlerRandom | Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen) |
| PointCloudColorHandlerCustom | Handler for predefined user colors |
| PointCloudColorHandlerRGBField | RGB handler class for colors |
| PointCloudColorHandlerHSVField | HSV handler class for colors |
| PointCloudColorHandlerGenericField | Generic field handler class for colors |
| PointCloudColorHandler< sensor_msgs::PointCloud2 > | Base Handler class for PointCloud colors |
| PointCloudColorHandlerRandom< sensor_msgs::PointCloud2 > | Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen) |
| PointCloudColorHandlerCustom< sensor_msgs::PointCloud2 > | Handler for predefined user colors |
| PointCloudColorHandlerRGBField< sensor_msgs::PointCloud2 > | RGB handler class for colors |
| PointCloudColorHandlerHSVField< sensor_msgs::PointCloud2 > | HSV handler class for colors |
| PointCloudColorHandlerGenericField< sensor_msgs::PointCloud2 > | Generic field handler class for colors |
| PointPickingCallback | |
| PointPickingEvent | /brief Class representing 3D point picking events |
| RangeImageVisualizer | Range image visualizer class |
| PCLImageCanvasSource2D | PclImageCanvasSource2D represents our own custom version of vtkImageCanvasSource2D, used by the ImageViewer class |
| Window | |
| ChannelProperties | ChannelProperties stores the properties of each channel in a cloud, namely: |
| CloudProperties | CloudProperties stores a list of optional point cloud properties such as: |
| BivariatePolynomialT | This represents a bivariate polynomial and provides some functionality for it |
| NdCentroidFunctor | Helper functor structure for n-D centroid estimation |
| NdConcatenateFunctor | Helper functor structure for concatenate |
| GaussianKernel | Class GaussianKernel assembles all the method for computing, convolving, smoothing, gradients computing an image using a gaussian kernel |
| PCA | Principal Component analysis (PCA) class |
| PiecewiseLinearFunction | This provides functionalities to efficiently return values for piecewise linear function |
| PolynomialCalculationsT | This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials |
| Parameters | Parameters used in this class |
| PosesFromMatches | Calculate 3D transformation based on point correspondencdes |
| Parameters | Parameters used in this class |
| PoseEstimate | A result of the pose estimation process |
| IsBetter | |
| Synchronizer | /brief This template class synchronizes two data streams of different types |
| StopWatch | Simple stopwatch |
| ScopeTime | Class to measure the time spent in a scope |
| TimeTrigger | Timer class that invokes registered callback methods periodically |
| TransformationFromCorrespondences | Calculates a transformation based on corresponding 3D points |
| VectorAverage | Calculates the weighted average and the covariance matrix |
| Correspondence | Correspondence represents a match between two entities (e.g., points, descriptors, etc) |
| PointCorrespondence3D | Representation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g |
| PointCorrespondence6D | Representation of a (possible) correspondence between two points (e.g |
| PCLException | A base class for all pcl exceptions which inherits from std::runtime_error |
| InvalidConversionException | An exception that is thrown when a PointCloud2 message cannot be converted into a PCL type |
| IsNotDenseException | An exception that is thrown when a PointCloud is not dense but is attemped to be used as dense |
| InvalidSACModelTypeException | An exception that is thrown when a sample consensus model doesn't have the correct number of samples defined in model_types.h |
| IOException | An exception that is thrown during an IO error (typical read/write errors) |
| InitFailedException | An exception thrown when init can not be performed should be used in all the PCLBase class inheritants |
| UnorganizedPointCloudException | An exception that is thrown when an organized point cloud is needed but not provided |
| KernelWidthTooSmallException | An exception that is thrown when the kernel size is too small |
| UnhandledPointTypeException | |
| ComputeFailedException | |
| for_each_type_impl | |
| for_each_type_impl< false > | |
| intersect | |
| _PointXYZ | |
| PointXYZ | A point structure representing Euclidean xyz coordinates |
| RGB | A structure representing RGB color information |
| _PointXYZI | A point structure representing Euclidean xyz coordinates, and the intensity value |
| PointXYZI | |
| _PointXYZL | |
| PointXYZL | |
| Label | |
| _PointXYZRGBA | A point structure representing Euclidean xyz coordinates, and the RGBA color |
| PointXYZRGBA | |
| _PointXYZRGB | |
| _PointXYZRGBL | |
| PointXYZRGB | A point structure representing Euclidean xyz coordinates, and the RGB color |
| PointXYZRGBL | |
| _PointXYZHSV | |
| PointXYZHSV | |
| PointXY | A 2D point structure representing Euclidean xy coordinates |
| InterestPoint | A point structure representing an interest point with Euclidean xyz coordinates, and an interest value |
| _Normal | A point structure representing normal coordinates and the surface curvature estimate |
| Normal | |
| _Axis | A point structure representing an Axis using its normal coordinates |
| Axis | |
| _PointNormal | A point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate |
| PointNormal | |
| _PointXYZRGBNormal | A point structure representing Euclidean xyz coordinates, and the RGB color, together with normal coordinates and the surface curvature estimate |
| PointXYZRGBNormal | |
| _PointXYZINormal | A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate |
| PointXYZINormal | |
| _PointWithRange | A point structure representing Euclidean xyz coordinates, padded with an extra range float |
| PointWithRange | |
| _PointWithViewpoint | |
| PointWithViewpoint | A point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen |
| MomentInvariants | A point structure representing the three moment invariants |
| PrincipalRadiiRSD | A point structure representing the minimum and maximum surface radii (in meters) computed using RSD |
| Boundary | A point structure representing a description of whether a point is lying on a surface boundary or not |
| PrincipalCurvatures | A point structure representing the principal curvatures and their magnitudes |
| PFHSignature125 | A point structure representing the Point Feature Histogram (PFH) |
| PFHRGBSignature250 | A point structure representing the Point Feature Histogram with colors (PFHRGB) |
| PPFSignature | A point structure for storing the Point Pair Feature (PPF) values |
| PPFRGBSignature | A point structure for storing the Point Pair Color Feature (PPFRGB) values |
| NormalBasedSignature12 | A point structure representing the Normal Based Signature for a feature matrix of 4-by-3 |
| ShapeContext | A point structure representing a Shape Context |
| SHOT | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) |
| SHOT352 | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape only |
| SHOT1344 | A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape+color |
| _ReferenceFrame | A structure representing the Local Reference Frame of a point |
| ReferenceFrame | |
| FPFHSignature33 | A point structure representing the Fast Point Feature Histogram (FPFH) |
| VFHSignature308 | A point structure representing the Viewpoint Feature Histogram (VFH) |
| ESFSignature640 | A point structure representing the Ensemble of Shape Functions (ESF) |
| GFPFHSignature16 | A point structure representing the GFPFH descriptor with 16 bins |
| Narf36 | A point structure representing the Narf descriptor |
| BorderDescription | A structure to store if a point in a range image lies on a border between an obstacle and the background |
| IntensityGradient | A point structure representing the intensity gradient of an XYZI point cloud |
| Histogram | A point structure representing an N-D histogram |
| _PointWithScale | A point structure representing a 3-D position and scale |
| PointWithScale | |
| _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 |
| PointSurfel | |
| ModelCoefficients | |
| PCLBase | PCL base class |
| PCLBase< sensor_msgs::PointCloud2 > | |
| PointCloud | PointCloud represents the base class in PCL for storing collections of 3D points |
| NdCopyEigenPointFunctor | Helper functor structure for copying data between an Eigen type and a PointT |
| NdCopyPointEigenFunctor | Helper functor structure for copying data between an Eigen type and a PointT |
| PointCloud< Eigen::MatrixXf > | PointCloud specialization for Eigen matrices |
| PointRepresentation | PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector |
| DefaultPointRepresentation | DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types |
| DefaultFeatureRepresentation | DefaulFeatureRepresentation extends PointRepresentation and is intended to be used when defining the default behavior for feature descriptor types (i.e., copy each element of each field into a float array) |
| DefaultPointRepresentation< PointXYZ > | |
| DefaultPointRepresentation< PointXYZI > | |
| DefaultPointRepresentation< PointNormal > | |
| DefaultPointRepresentation< PFHSignature125 > | |
| DefaultPointRepresentation< PFHRGBSignature250 > | |
| DefaultPointRepresentation< PPFSignature > | |
| DefaultPointRepresentation< FPFHSignature33 > | |
| DefaultPointRepresentation< VFHSignature308 > | |
| DefaultPointRepresentation< NormalBasedSignature12 > | |
| DefaultPointRepresentation< ShapeContext > | |
| DefaultPointRepresentation< SHOT352 > | |
| DefaultPointRepresentation< SHOT1344 > | |
| CustomPointRepresentation | CustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point |
| FieldMatches | |
| CopyIfFieldExists | A helper functor that can copy a specific value if the given field exists |
| SetIfFieldExists | A helper functor that can set a specific value in a field if the field exists |
| PointIndices | |
| PolygonMesh | |
| 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 |
| 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 |
| TexMaterial | |
| RGB | |
| TextureMesh | |
| Vertices | Describes a set of vertices in a polygon mesh, by basically storing an array of indices |
| ShapeContext3DEstimation | ShapeContext3DEstimation implements the 3D shape context descriptor as described in: |
| ShapeContext3DEstimation< PointInT, PointNT, Eigen::MatrixXf > | ShapeContext3DEstimation implements the 3D shape context descriptor as described in: |
| BoundaryEstimation | BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion |
| BoundaryEstimation< PointInT, PointNT, Eigen::MatrixXf > | BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion |
| CVFHEstimation | CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in: |
| ESFEstimation | ESFEstimation estimates the ensemble of shape functions descriptors for a given point cloud dataset containing points |
| Feature | Feature represents the base feature class |
| FeatureFromNormals | |
| FeatureFromLabels | |
| FeatureWithLocalReferenceFrames | FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which need a local reference frame at each input keypoint |
| FPFHEstimation | FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals |
| FPFHEstimation< PointInT, PointNT, Eigen::MatrixXf > | FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals |
| FPFHEstimationOMP | FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |
| IntegralImageTypeTraits | |
| IntegralImageTypeTraits< float > | |
| IntegralImageTypeTraits< char > | |
| IntegralImageTypeTraits< short > | |
| IntegralImageTypeTraits< unsigned short > | |
| IntegralImageTypeTraits< unsigned char > | |
| IntegralImageTypeTraits< int > | |
| IntegralImageTypeTraits< unsigned int > | |
| IntegralImage2D | Determines an integral image representation for a given organized data array |
| IntegralImage2D< DataType, 1 > | Partial template specialization for integral images with just one channel |
| IntegralImageNormalEstimation | Surface normal estimation on organized data using integral images |
| IntensityGradientEstimation | IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values |
| IntensityGradientEstimation< PointInT, PointNT, Eigen::MatrixXf > | IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values |
| IntensitySpinEstimation | IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity |
| IntensitySpinEstimation< PointInT, Eigen::MatrixXf > | IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity |
| MomentInvariantsEstimation | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |
| MomentInvariantsEstimation< PointInT, Eigen::MatrixXf > | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |
| MultiscaleFeaturePersistence | Generic class for extracting the persistent features from an input point cloud It can be given any Feature estimator instance and will compute the features of the input over a multiscale representation of the cloud and output the unique ones over those scales |
| Narf | NARF (Normal Aligned Radial Features) is a point feature descriptor type for 3D data |
| FeaturePointRepresentation | |
| NarfDescriptor | Computes NARF feature descriptors for points in a range image |
| Parameters | |
| NormalEstimation | NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point |
| NormalEstimation< PointInT, Eigen::MatrixXf > | NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures |
| NormalEstimationOMP | NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard |
| NormalEstimationOMP< PointInT, Eigen::MatrixXf > | NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard |
| NormalBasedSignatureEstimation | Normal-based feature signature estimation class |
| PFHEstimation | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |
| PFHEstimation< PointInT, PointNT, Eigen::MatrixXf > | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |
| PFHRGBEstimation | |
| PPFEstimation | Class that calculates the "surflet" features for each pair in the given pointcloud |
| PPFEstimation< PointInT, PointNT, Eigen::MatrixXf > | Class that calculates the "surflet" features for each pair in the given pointcloud |
| PPFRGBEstimation | |
| PPFRGBRegionEstimation | |
| PrincipalCurvaturesEstimation | PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals |
| PrincipalCurvaturesEstimation< PointInT, PointNT, Eigen::MatrixXf > | PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals |
| RangeImageBorderExtractor | Extract obstacle borders from range images, meaning positions where there is a transition from foreground to background |
| LocalSurface | Stores some information extracted from the neighborhood of a point |
| Parameters | Parameters used in this class |
| ShadowBorderIndices | Stores the indices of the shadow border corresponding to obstacle borders |
| RIFTEstimation | RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity |
| RIFTEstimation< PointInT, GradientT, Eigen::MatrixXf > | RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity |
| RSDEstimation | RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves) for a given point cloud dataset containing points and normals |
| SHOTEstimationBase | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |
| SHOTEstimation | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals |
| SHOTLocalReferenceFrameEstimation | SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor |
| SHOTLocalReferenceFrameEstimationOMP | SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor |
| SHOTEstimationOMP | SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |
| SHOTColorEstimationOMP | |
| SpinImageEstimation | Estimates spin-image descriptors in the given input points |
| SpinImageEstimation< PointInT, PointNT, Eigen::MatrixXf > | Estimates spin-image descriptors in the given input points |
| StatisticalMultiscaleInterestRegionExtraction | Class for extracting interest regions from unstructured point clouds, based on a multi scale statistical approach |
| UniqueShapeContext | UniqueShapeContext implements the Unique Shape Descriptor described here: |
| UniqueShapeContext< PointInT, Eigen::MatrixXf, PointRFT > | UniqueShapeContext implements the Unique Shape Descriptor described here: |
| VFHEstimation | VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals |
| xNdCopyEigenPointFunctor | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |
| xNdCopyPointEigenFunctor | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |
| ApproximateVoxelGrid | ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| BilateralFilter | A bilateral filter implementation for point cloud data |
| Clipper3D | Base class for 3D clipper objects |
| PointDataAtOffset | A datatype that enables type-correct comparisons |
| ComparisonBase | The (abstract) base class for the comparison object |
| FieldComparison | The field-based specialization of the comparison object |
| PackedRGBComparison | A packed rgb specialization of the comparison object |
| PackedHSIComparison | A packed HSI specialization of the comparison object |
| TfQuadraticXYZComparison | A comparison whether the (x,y,z) components of a given point satisfy (p'Ap + 2v'p + c [OP] 0) |
| ConditionBase | Base condition class |
| ConditionAnd | AND condition |
| ConditionOr | OR condition |
| ConditionalRemoval | ConditionalRemoval filters data that satisfies certain conditions |
| CropBox | CropBox is a filter that allows the user to filter all the data inside of a given box |
| CropBox< sensor_msgs::PointCloud2 > | CropBox is a filter that allows the user to filter all the data inside of a given box |
| CropHull | Filter points that lie inside or outside a 3D closed surface or 2D closed polygon, as generated by the ConvexHull or ConcaveHull classes |
| ExtractIndices | ExtractIndices extracts a set of indices from a point cloud |
| ExtractIndices< sensor_msgs::PointCloud2 > | ExtractIndices extracts a set of indices from a point cloud |
| Filter | Filter represents the base filter class |
| Filter< sensor_msgs::PointCloud2 > | Filter represents the base filter class |
| FilterIndices | FilterIndices represents the base class for filters that are about binary point removal |
| FilterIndices< sensor_msgs::PointCloud2 > | FilterIndices represents the base class for filters that are about binary point removal |
| NormalSpaceSampling | NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every point |
| PassThrough | PassThrough passes points in a cloud based on constraints for one particular field of the point type |
| PassThrough< sensor_msgs::PointCloud2 > | PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints |
| PlaneClipper3D | Implementation of a plane clipper in 3D |
| ProjectInliers | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |
| ProjectInliers< sensor_msgs::PointCloud2 > | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |
| RadiusOutlierRemoval | RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have |
| 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 |
| RandomSample | RandomSample applies a random sampling with uniform probability |
| RandomSample< sensor_msgs::PointCloud2 > | RandomSample applies a random sampling with uniform probability |
| StatisticalOutlierRemoval | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |
| StatisticalOutlierRemoval< sensor_msgs::PointCloud2 > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data |
| VoxelGrid | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| VoxelGrid< sensor_msgs::PointCloud2 > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| LineIterator | Organized Index Iterator for iterating over the "pixels" for a given line using the Bresenham algorithm |
| OrganizedIndexIterator | Base class for iterators on 2-dimensional maps like images/organized clouds etc |
| PlanarPolygon | PlanarPolygon represents a planar (2D) polygon, potentially in a 3D space |
| AdaptiveRangeCoder | AdaptiveRangeCoder compression class |
| StaticRangeCoder | StaticRangeCoder compression class |
| FileReader | Point Cloud Data (FILE) file format reader interface |
| FileWriter | Point Cloud Data (FILE) file format writer |
| Grabber | Grabber interface for PCL 1.x device drivers |
| PCDGrabberBase | Base class for PCD file grabber |
| PCDGrabber | |
| PCDReader | Point Cloud Data (PCD) file format reader |
| PCDWriter | Point Cloud Data (PCD) file format writer |
| PCLIOException | Base exception class for I/O operations |
| PLYReader | Point Cloud Data (PLY) file format reader |
| PLYWriter | Point Cloud Data (PLY) file format writer |
| KdTree | KdTree represents the base spatial locator class for kd-tree implementations |
| KdTreeFLANN | KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures |
| KdTreeFLANN< Eigen::MatrixXf > | KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures |
| HarrisKeypoint3D | HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals |
| Keypoint | Keypoint represents the base class for key points |
| NarfKeypoint | NARF (Normal Aligned Radial Feature) keypoints |
| Parameters | Parameters used in this class |
| SIFTKeypointFieldSelector | |
| SIFTKeypointFieldSelector< PointNormal > | |
| SIFTKeypointFieldSelector< PointXYZRGB > | |
| SIFTKeypointFieldSelector< PointXYZRGBA > | |
| SIFTKeypoint | SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity |
| SmoothedSurfacesKeypoint | Based on the paper: Xinju Li and Igor Guskov Multi-scale features for approximate alignment of point-based surfaces Proceedings of the third Eurographics symposium on Geometry processing July 2005, Vienna, Austria |
| UniformSampling | UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| SolverDidntConvergeException | An exception that is thrown when the non linear solver didn't converge |
| NotEnoughPointsException | An exception that is thrown when the number of correspondants is not equal to the minimum required |
| GeneralizedIterativeClosestPoint | GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al |
| SampleConsensusInitialAlignment | SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al |
| ErrorFunctor | |
| HuberPenalty | |
| TruncatedError | |
| IterativeClosestPoint | IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm |
| IterativeClosestPointNonLinear | IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend |
| PPFHashMapSearch | |
| HashKeyStruct | Data structure to hold the information for the key in the feature hash map of the PPFHashMapSearch class |
| PPFRegistration | Class that registers two point clouds based on their sets of PPFSignatures |
| PoseWithVotes | Structure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes |
| PyramidFeatureHistogram | Class that compares two sets of features by using a multiscale representation of the features inside a pyramid |
| Registration | Registration represents the base registration class |
| WarpPointRigid | |
| WarpPointRigid3D | |
| WarpPointRigid6D | |
| LeastMedianSquares | LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm |
| MaximumLikelihoodSampleConsensus | MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to
estimating image geometry", P.H.S |
| MEstimatorSampleConsensus | MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S |
| ProgressiveSampleConsensus | RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Matching with PROSAC – Progressive Sample Consensus", Chum, O |
| RandomSampleConsensus | RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and
Automated Cartography", Martin A |
| RandomizedMEstimatorSampleConsensus | RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus) |
| RandomizedRandomSampleConsensus | RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RAndom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O |
| SampleConsensus | SampleConsensus represents the base class |
| SampleConsensusModel | SampleConsensusModel represents the base model class |
| SampleConsensusModelFromNormals | SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation |
| Functor | Base functor all the models that need non linear optimization must define their own one and implement operator() (const Eigen::VectorXd& x, Eigen::VectorXd& fvec) or operator() (const Eigen::VectorXf& x, Eigen::VectorXf& fvec) dependening on the choosen _Scalar |
| SampleConsensusModelCircle2D | SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane |
| SampleConsensusModelCone | SampleConsensusModelCone defines a model for 3D cone segmentation |
| SampleConsensusModelCylinder | SampleConsensusModelCylinder defines a model for 3D cylinder segmentation |
| SampleConsensusModelLine | SampleConsensusModelLine defines a model for 3D line segmentation |
| SampleConsensusModelNormalParallelPlane | SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints |
| SampleConsensusModelNormalPlane | SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints |
| SampleConsensusModelNormalSphere | SampleConsensusModelNormalSphere defines a model for 3D sphere segmentation using additional surface normal constraints |
| SampleConsensusModelParallelLine | SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints |
| SampleConsensusModelParallelPlane | SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints |
| SampleConsensusModelPerpendicularPlane | SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints |
| SampleConsensusModelPlane | SampleConsensusModelPlane defines a model for 3D plane segmentation |
| SampleConsensusModelRegistration | SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection |
| SampleConsensusModelSphere | SampleConsensusModelSphere defines a model for 3D sphere segmentation |
| SampleConsensusModelStick | SampleConsensusModelStick defines a model for 3D stick segmentation |
| Comparator | Comparator is the base class for comparators that compare two points given some function |
| EdgeAwarePlaneComparator | EdgeAwarePlaneComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| EuclideanClusterComparator | EuclideanClusterComparator is a comparator used for finding clusters supported by planar surfaces |
| EuclideanPlaneCoefficientComparator | EuclideanPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| EuclideanClusterExtraction | EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense |
| LabeledEuclideanClusterExtraction | LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info |
| ExtractPolygonalPrismData | ExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given height, generates a 3D polygonal prism |
| OrganizedConnectedComponentSegmentation | OrganizedConnectedComponentSegmentation allows connected components to be found within organized point cloud data, given a comparison function |
| OrganizedMultiPlaneSegmentation | 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 |
| PlanarPolygonFusion | 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 |
| PlanarRegion | PlanarRegion represents a set of points that lie in a plane |
| PlaneCoefficientComparator | PlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| PlaneRefinementComparator | PlaneRefinementComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| Region3D | Region3D represents summary statistics of a 3D collection of points |
| RGBPlaneCoefficientComparator | RGBPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation |
| SACSegmentation | SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation |
| SACSegmentationFromNormals | SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation |
| SegmentDifferences | SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold |
| BilateralUpsampling | Bilateral filtering implementation, based on the following paper: |
| EarClipping | The ear clipping triangulation algorithm |
| GreedyProjectionTriangulation | GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections |
| GridProjection | Grid projection surface reconstruction method |
| Leaf | Data leaf |
| MarchingCubes | The marching cubes surface reconstruction algorithm |
| MarchingCubesHoppe | The marching cubes surface reconstruction algorithm, using a signed distance function based on the distance from tangent planes, proposed by Hoppe et |
| MarchingCubesRBF | The marching cubes surface reconstruction algorithm, using a signed distance function based on radial basis functions |
| MovingLeastSquares | MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation |
| MovingLeastSquaresOMP | MovingLeastSquaresOMP represent an OpenMP implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation |
| OrganizedFastMesh | Simple triangulation/surface reconstruction for organized point clouds |
| Poisson | The Poisson surface reconstruction algorithm |
| CloudSurfaceProcessing | CloudSurfaceProcessing represents the base class for algorithms that take a point cloud as an input and produce a new output cloud that has been modified towards a better surface representation |
| MeshProcessing | MeshProcessing represents the base class for mesh processing algorithms |
| PCLSurfaceBase | Pure abstract class |
| SurfaceReconstruction | SurfaceReconstruction represents a base surface reconstruction class |
| MeshConstruction | MeshConstruction represents a base surface reconstruction class |
| SurfelSmoothing | |
| TextureMapping | The texture mapping algorithm |
| MeshSmoothingLaplacianVTK | PCL mesh smoothing based on the vtkSmoothPolyDataFilter algorithm from the VTK library |
| MeshSmoothingWindowedSincVTK | PCL mesh smoothing based on the vtkWindowedSincPolyDataFilter algorithm from the VTK library |
| MeshSubdivisionVTK | PCL mesh smoothing based on the vtkLinearSubdivisionFilter, vtkLoopSubdivisionFilter, vtkButterflySubdivisionFilter depending on the selected MeshSubdivisionVTKFilterType algorithm from the VTK library |
| VTKUtils | |
| RegistrationVisualizer | RegistrationVisualizer represents the base class for rendering the intermediate positions ocupied by the source point cloud during it's registration to the target point cloud |
| sensor_msgs | |
| Image | |
| PointCloud2 | |
| PointField | |
| std_msgs | |
| Header | |
| cloud_point_index_idx | |
| Mesh | |
| ObjectFeatures | |
| ObjectModel | |
| ObjectRecognition | |
| ObjectRecognitionParameters | |
| OpenNICapture | |
1.8.4
