Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 123]
 Npcl
 Ndetail
 CFieldsLength
 CFieldStreamer
 Npcl_ros
 CBAGReaderBAG PointCloud file format reader
 CBasePublisher
 CBoundaryEstimationBoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion. The code makes use of the estimated surface normals at each point in the input dataset
 CConvexHull2DConvexHull2D represents a 2D ConvexHull implementation
 CCropBoxCropBox is a filter that allows the user to filter all the data inside of a given box
 CEuclideanClusterExtractionEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense
 CExtractIndicesExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud
 CExtractPolygonalPrismDataExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given height, generates a 3D polygonal prism. The polygonal prism is then used to segment all points lying inside it
 CFeatureFeature represents the base feature class. Some generic 3D operations that are applicable to all features are defined here as static methods
 CFeatureFromNormals
 CFilterFilter represents the base filter class. Some generic 3D operations that are applicable to all filters are defined here as static methods
 CFPFHEstimationFPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals
 CFPFHEstimationOMPFPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard
 CMomentInvariantsEstimationMomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point
 CMovingLeastSquaresMovingLeastSquares represents a nodelet using the MovingLeastSquares implementation. The type of the output is the same as the input, it only smooths the XYZ coordinates according to the parameters. Normals are estimated at each point as well and published on a separate topic
 CNormalEstimationNormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures
 CNormalEstimationOMPNormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard
 CNormalEstimationTBBNormalEstimationTBB estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using Intel's Threading Building Blocks library
 CPassThroughPassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
 CPCDReaderPoint Cloud Data (PCD) file format reader
 CPCDWriterPoint Cloud Data (PCD) file format writer
 CPCLNodeletPCLNodelet represents the base PCL Nodelet class. All PCL nodelets should inherit from this class
 CPFHEstimationPFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals
 CPointCloudConcatenateDataSynchronizerPointCloudConcatenateFieldsSynchronizer is a special form of data synchronizer: it listens for a set of input PointCloud messages on the same topic, checks their timestamps, and concatenates their fields together into a single PointCloud output message
 CPointCloudConcatenateFieldsSynchronizerPointCloudConcatenateFieldsSynchronizer is a special form of data synchronizer: it listens for a set of input PointCloud messages on the same topic, checks their timestamps, and concatenates their fields together into a single PointCloud output message
 CPrincipalCurvaturesEstimationPrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals
 CProjectInliersProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud
 CPublisher
 CPublisher< sensor_msgs::PointCloud2 >
 CRadiusOutlierRemovalRadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K
 CSACSegmentationSACSegmentation 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
 CSACSegmentationFromNormalsSACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation
 CSegmentDifferencesSegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold
 CSHOTEstimationSHOTEstimation estimates SHOT descriptor
 CSHOTEstimationOMPSHOTEstimation estimates SHOT descriptor using OpenMP
 CStatisticalOutlierRemovalStatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check:
 CVFHEstimationVFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals
 CVoxelGridVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
 Nros
 Nmessage_traits
 CDataType< pcl::PointCloud< T > >
 CDefinition< pcl::PointCloud< T > >
 CFrameId< pcl::PointCloud< T > >
 CHasHeader< pcl::PointCloud< T > >
 CMD5Sum< pcl::PointCloud< T > >
 CTimeStamp< pcl::PointCloud< T > >
 Nserialization
 CSerializer< pcl::PointCloud< T > >
 CPCDGenerator
 CPointCloudToImage
 CPointCloudToPCD


pcl_ros
Author(s): Open Perception, Julius Kammerl , William Woodall
autogenerated on Mon Jun 10 2019 14:19:19