Definition at line 442 of file point_cloud.h. Typical values include 32 and 64. Definition at line 471 of file point_cloud.h. M-by-N-by-3 arrays, with the three channels I have an organized point cloud stored in a pcl::PointCloud
data structure. If you only need to write few attributes of a point cloud or mesh, the quickest way to use the save_mesh_* functions * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER, * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT, * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN, * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE, * $Id: example_OrganizedPointCloud.cpp 4258 2012-02-05 15:06:20Z daviddoria $. CTRL-K PointCloud ROS Examples Suggest Edits 1. to open up point cloud files and plot them using standard software tools like Iteratively search for the blank points, and manually convert their coordinates to NaN, so they won't be used during mesh generation. graphics and computational geometry communities in particular, have created uniform beam (laser scanner) configuration. When it moves from one column of data to the next, it fills in any gaps with NaN points. The point cloud height (if organized as an image-structure). Why is it so much harder to run on a treadmill when not holding the handlebars? example, x data usually has 1 element, but a feature descriptor like the The data is divided according to the spatial relationships between the points. The official entry point for the PCD file format in PCL however should be Definition at line 418 of file point_cloud.h. Definition at line 436 of file point_cloud.h. Examples of such point clouds include data Removes all points in a cloud and sets the width and height to 0. Definition at line 199 of file point_cloud.h. SalsaNext, process only organized point clouds. in ASCII. An organized point cloud dataset is the name given to point clouds that resemble an organized image (or matrix) like structure, where the data is split into rows and columns. Definition at line 440 of file point_cloud.h. If I didn't get it wrong, organized point clouds should be stored in a matrix-like structure. Definition at line 425 of file point_cloud.h. pcorganize default, if COUNT is not present, all dimensions count is set to 1. Definition at line 441 of file point_cloud.h. Why do American universities have so many gen-eds? As point clouds based on the shape of their data. surface normals, that need a consistent orientation. Instead of accessing it in the usual way, i.e., as a linear array. Definition at line 448 of file point_cloud.h. interpret the data and see what it means. This could potentially be later on used for building transforms Asking for help, clarification, or responding to other answers. Allow non-GPL plugins in a GPL main program. Definition at line 444 of file point_cloud.h. Organized PCD format The Zivid SDK stores the ordered point cloud with a header that indicates an unordered point cloud. support some of the extensions that PCL brings to n-D point cloud processing. Though PCD (Point Cloud Data) is the native file format in PCL, the If I didn't get it wrong, organized point clouds should be stored in a matrix-like structure. You can accces the points with () operator, Let i be the element number which you want to access. These types should be enough to support all the algorithms and methods implemented in PCL. Definition at line 429 of file point_cloud.h. Referenced by pcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >::getEdgeIndex(), pcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >::getFaceIndex(), pcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >::getHalfEdgeIndex(), and pcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >::getVertexIndex(). This makes organized point cloud An organized point cloud resembles a 2-D matrix, with its data divided into rows and columns. PCD is a file format native for Point Cloud Library. algorithms had been invented. Accelerating the pace of engineering and science. The PCD file format is not meant to reinvent the wheel, but rather to Definition at line 401 of file point_cloud.h. The sensor in the You can also select a web site from the following list: Select the China site (in Chinese or English) for best site performance. When the laser scanners are stacked with equal spacing, the lidar sensor has a Other MathWorks country sites are not optimized for visits from your location. Thanks for contributing an answer to Stack Overflow! What are Organized and Unorganized Point Clouds? Copy constructor from point cloud subset. See here http://docs.pointclouds.org/trunk/class . * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS, * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT, * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. gradient beam configuration. Definition at line 423 of file point_cloud.h. Referenced by pcl::Registration< PointSource, PointTarget, Scalar >::align(), pcl::CovarianceSampling< PointT, PointNT >::applyFilter(), pcl::ConditionalRemoval< PointT >::applyFilter(), pcl::ESFEstimation< PointInT, PointOutT >::compute(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::compute(), pcl::VFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::Feature< PointInT, PointOutT >::compute(), pcl::features::computeApproximateNormals(), pcl::concatenateFields(), pcl::copyPointCloud(), pcl::demeanPointCloud(), pcl::SmoothedSurfacesKeypoint< PointT, PointNT >::detectKeypoints(), pcl::ISSKeypoint3D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::MultiscaleFeaturePersistence< PointSource, PointFeature >::determinePersistentFeatures(), pcl::extractEuclideanClusters(), pcl::extractLabeledEuclideanClusters(), pcl::Filter< pcl::PointXYZRGBL >::filter(), pcl::fromPCLPointCloud2(), pcl::getPointCloudDifference(), pcl::PointCloud< ModelT >::operator+=(), pcl::operator<<(), pcl::BilateralUpsampling< PointInT, PointOutT >::performProcessing(), pcl::GridProjection< PointNT >::performReconstruction(), pcl::CloudSurfaceProcessing< PointInT, PointOutT >::process(), pcl::BilateralUpsampling< PointInT, PointOutT >::process(), pcl::MovingLeastSquares< PointInT, PointOutT >::process(), pcl::SampleConsensusModelLine< PointT >::projectPoints(), pcl::SampleConsensusModelStick< PointT >::projectPoints(), pcl::SampleConsensusModelCircle2D< PointT >::projectPoints(), pcl::SampleConsensusModelCircle3D< PointT >::projectPoints(), pcl::SampleConsensusModelSphere< PointT >::projectPoints(), pcl::SampleConsensusModelCylinder< PointT, PointNT >::projectPoints(), pcl::SampleConsensusModelPlane< PointT >::projectPoints(), pcl::SampleConsensusModelCone< PointT, PointNT >::projectPoints(), pcl::ConcaveHull< PointInT >::reconstruct(), pcl::ConvexHull< PointInT >::reconstruct(), pcl::SurfaceReconstruction< PointInT >::reconstruct(), pcl::removeNaNFromPointCloud(), pcl::removeNaNNormalsFromPointCloud(), pcl::SegmentDifferences< PointT >::segment(), pcl::toPCLPointCloud2(), pcl::transformPointCloud(), and pcl::transformPointCloudWithNormals(). As a result, the memory layout of an organized point cloud relates to the Isn't there a great PCL trick to simplify this? Referenced by pcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >::addData(), pcl::VoxelGridCovariance< PointT >::applyFilter(), pcl::approximatePolygon2D(), pcl::MovingLeastSquares< PointInT, PointOutT >::computeMLSPointNormal(), pcl::TSDFVolume< VoxelT, WeightT >::convertToTsdfCloud(), pcl::MarchingCubes< PointNT >::createSurface(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::TrajkovicKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::TrajkovicKeypoint3D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::BriskKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::gpu::extractEuclideanClusters(), pcl::gpu::extractLabeledEuclideanClusters(), pcl::VoxelGridCovariance< PointT >::getDisplayCloud(), pcl::MovingLeastSquares< PointInT, PointOutT >::performUpsampling(), pcl::PCA< PointT >::project(), pcl::PCA< PointT >::reconstruct(), pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT >::segment(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::setPointsToTrack(), and pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::track(). To learn more, see our tips on writing great answers. Some of the clearly stated advantages include: An additional advantage is that by controlling the file format, we can best Definition at line 415 of file point_cloud.h. between different coordinate systems, or for aiding with features such as The underlying algorithm uses spherical projection to represent the 3-D point Similarly, cloud->points[i].y and cloud->points[i].z will give you the y and z coordinates. : Copyright Only works on organized datasets (those that have height != 1). Definition at line 469 of file point_cloud.h. By saying pointcloud data, I mean depth data + RGB data - if you combine these two you get exactly the same as in realsense viewer, when you hit the 3D button. For example, to create a point cloud that holds 4 random XYZ data points, use: pcl::PointCloud<pcl::PointXYZ> cloud; The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing. preceding picture has a vertical field of view of 45 degrees. PCLPoint Cloud LibraryC++ WindowsLinuxAndroidMac OS X . points (e.g. That is not specific to organized pointclouds. Definition at line 352 of file point_cloud.h. Web browsers do not support MATLAB commands. is used inside Point Cloud Library (PCL). A snippet of a PCD file is attached below. spatial layout represented by the xyz-coordinates of its points. The data is divided according to the spatial relationships between The type of file is inferred from its file extension. We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. Learn more about bidirectional Unicode characters. parameters for some popular lidar sensors. algorithms in PCL. Does the collective noun "parliament of owls" originate in "parliament of fowls"? organized point cloud resembles a 2-D matrix, with its data divided Definition at line 413 of file point_cloud.h. Definition at line 322 of file point_cloud.h. PCL-ROS is the preferred bridge for 3D applications involving n-D Point Clouds and 3D geometry processing in ROS. space or tab separated, without any other characters on it, as well as in a If the given cloud is structured it will have e.g. * Software License Agreement (BSD License), * Point Cloud Library (PCL) - www.pointclouds.org. adapt it to PCL, and thus obtain the highest performance with respect to PCL type and inducing additional delays through conversion functions. Point-Cloud-Utils supports writing many common mesh formats (PLY, STL, OFF, OBJ, 3DS, VRML 2.0, X3D, COLLADA). My function creates a 2D organized cloud where there is one row for each "ring" of velodyne data. You can also differentiate these Insert a new range of points in the cloud, at a certain position. Examples: TYPE - specifies the type of each dimension as a char. How do I tell if this single climbing rope is still safe for use? The class is templated, which means you need to specify the type of data that it should contain. # Image-like organized structure, with 480 rows and 640 columns, # thus 640*480=307200 points total in the dataset, # unorganized point cloud dataset with 307200 points, # the total number of points in the cloud, # .PCD v.7 - Point Cloud Data file format, it can specify the total number of points in the cloud (equal with. Definition at line 437 of file point_cloud.h. These describe point The demo will capture a single depth frame from the camera, convert it to pcl::PointCloud object and perform basic PassThrough filter, but will capture the frame using a tuple for RGB color support. Maybe it would be a good idea to use the "Euclidean Cluster Extraction" to segment my preprocessed point cloud and then search each cluster to determine witch one would be my nearest obstacle? Since almost all classes in PCL inherit from the basic pcl::PCLBase class, the pcl::Feature class accepts input data in two different ways: A complete point cloud data set is forced through setinputcloud (pointcloudconstptr &) Any feature estimation class will attempt to estimate the features of each point in a given input cloud. There are two types of point clouds: organized and unorganized. Definition at line 376 of file point_cloud.h. Are there breakers which can be triggered by an external signal and have to be reset by hand? Referenced by pcl::visualization::ImageViewer::addMask(), pcl::visualization::ImageViewer::addPlanarPolygon(), pcl::visualization::PCLVisualizer::addPolygonMesh(), pcl::LineRGBD< PointXYZT, PointRGBT >::addTemplate(), pcl::recognition::TrimmedICP< pcl::pcl::PointXYZ, float >::align(), pcl::FastBilateralFilterOMP< PointT >::applyFilter(), pcl::CovarianceSampling< PointT, PointNT >::applyFilter(), pcl::approximatePolygon(), pcl::approximatePolygon2D(), pcl::calculatePolygonArea(), pcl::PlaneClipper3D< PointT >::clipPointCloud3D(), pcl::BoxClipper3D< PointT >::clipPointCloud3D(), pcl::compute3DCentroid(), pcl::computeCentroid(), pcl::computeCovarianceMatrix(), pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::computeCovariances(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::computeFeature(), pcl::computeMeanAndCovarianceMatrix(), pcl::computeNDCentroid(), pcl::computePointNormal(), pcl::LineRGBD< PointXYZT, PointRGBT >::computeTransformedTemplatePoints(), pcl::copyPointCloud(), pcl::LineRGBD< PointXYZT, PointRGBT >::createAndAddTemplate(), pcl::demeanPointCloud(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::derivatives(), pcl::Edge< PointInT, PointOutT >::detectEdgePrewitt(), pcl::Edge< PointInT, PointOutT >::detectEdgeRoberts(), pcl::Edge< PointInT, PointOutT >::detectEdgeSobel(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::TrajkovicKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::TrajkovicKeypoint3D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::BriskKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::estimateRigidTransformation(), pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::extractDescriptors(), pcl::gpu::extractEuclideanClusters(), pcl::gpu::extractLabeledEuclideanClusters(), pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::findObjects(), pcl::kernel< PointT >::gaussianKernel(), pcl::ISSKeypoint3D< PointInT, PointOutT, NormalT >::getBoundaryPoints(), pcl::getMeanPointDensity(), pcl::Morphology< PointT >::intersectionBinary(), pcl::LineRGBD< PointXYZT, PointRGBT >::loadTemplates(), pcl::kernel< PointT >::loGKernel(), pcl::search::Search< PointT >::nearestKSearch(), pcl::search::FlannSearch< PointT, FlannDistance >::nearestKSearch(), pcl::search::Search< PointXYZRGB >::nearestKSearchT(), pcl::MovingLeastSquares< PointInT, PointOutT >::performProcessing(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::GridProjection< PointNT >::performReconstruction(), pcl::MarchingCubes< PointNT >::performReconstruction(), pcl::PointCloud< ModelT >::PointCloud(), pcl::io::pointCloudTovtkPolyData(), pcl::MovingLeastSquares< PointInT, PointOutT >::process(), pcl::PCA< PointT >::project(), pcl::search::FlannSearch< PointT, FlannDistance >::radiusSearch(), pcl::search::Search< PointT >::radiusSearch(), pcl::search::Search< PointXYZRGB >::radiusSearchT(), pcl::io::LZFRGB24ImageReader::read(), pcl::io::LZFBayer8ImageReader::read(), pcl::io::LZFDepth16ImageReader::readOMP(), pcl::io::LZFRGB24ImageReader::readOMP(), pcl::io::LZFBayer8ImageReader::readOMP(), pcl::PCA< PointT >::reconstruct(), pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >::refineCorners(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::responseHarris(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::responseLowe(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::responseNoble(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::responseTomasi(), pcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >::setEdgeDataCloud(), pcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >::setFaceDataCloud(), pcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >::setHalfEdgeDataCloud(), pcl::SupervoxelClustering< PointT >::setInputCloud(), pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::setInputSource(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::setPointsToTrack(), pcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >::setVertexDataCloud(), pcl::Edge< PointInT, PointOutT >::sobelMagnitudeDirection(), pcl::Morphology< PointT >::structuringElementRectangle(), pcl::Morphology< PointT >::subtractionBinary(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::track(), pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >::transformCloud(), pcl::Morphology< PointT >::unionBinary(), pcl::visualization::PCLVisualizer::updatePolygonMesh(), pcl::registration::KFPCSInitialAlignment< PointSource, PointTarget, NormalT, Scalar >::validateTransformation(), and pcl::registration::FPCSInitialAlignment< PointSource, PointTarget, NormalT, Scalar >::validateTransformation(). Definition at line 331 of file point_cloud.h. representing the x-, y-, and z- It is left to the reader to As of version 0.7, the PCD header contains the following entries: FIELDS - specifies the name of each dimension/field that a point can Each laser scanner releases a laser pulse and rotates to of the sensor field of view are more spaced out, the lidar sensor has a The library contains algorithms for filtering, feature estimation, surface reconstruction, 3D registration, [4] model fitting, object recognition, and segmentation. using CloudType = pcl::PointCloud<PointType>; CloudType:: Ptr cloud ( new CloudType); // Make the cloud a 10x10 grid cloud-> height = 10; cloud-> width = 10; cloud-> is_dense = true; cloud-> resize (cloud-> height * cloud-> width ); // Output the (0,0) point std::cout << (*cloud) ( 0, 0) << std::endl; // Set the (0,0) point Definition at line 283 of file point_cloud.h. Why did the Council of Elrond debate hiding or sending the Ring away, if Sauron wins eventually in that scenario? Return an Eigen MatrixXf (assumes float values) mapped to the PointCloud. Definition at line 443 of file point_cloud.h. The next bytes directly after the headers last line (DATA) are Definition at line 575 of file point_cloud.h. It contains information about the acquisition time. cloud.html A point cloud has two public attribute width and height which works like width and height of an image. Definition at line 536 of file point_cloud.h. degrees. Definition at line 433 of file point_cloud.h. PCD file formats might have different revision numbers, prior to the release of Insert a new point in the cloud, given an iterator. configuration, you must specify these parameters from the sensor handbook: Vertical resolution Number of channels in the vertical direction, consisting WIDTH has two meanings: An organized point cloud dataset is the name given to point clouds that Referenced by pcl::visualization::PCLVisualizer::addCorrespondences(), pcl::visualization::PCLHistogramVisualizer::addFeatureHistogram(), pcl::visualization::PCLPlotter::addFeatureHistogram(), pcl::visualization::PCLVisualizer::addPointCloudIntensityGradients(), pcl::visualization::PCLVisualizer::addPointCloudNormals(), pcl::visualization::PCLVisualizer::addPointCloudPrincipalCurvatures(), pcl::visualization::PCLVisualizer::addPolygonMesh(), pcl::visualization::ImageViewer::addRectangle(), pcl::LineRGBD< PointXYZT, PointRGBT >::addTemplate(), pcl::recognition::TrimmedICP< pcl::pcl::PointXYZ, float >::align(), pcl::Registration< PointSource, PointTarget, Scalar >::align(), pcl::BilateralFilter< PointT >::applyFilter(), pcl::LocalMaximum< PointT >::applyFilter(), pcl::GridMinimum< PointT >::applyFilter(), pcl::ShadowPoints< PointT, NormalT >::applyFilter(), pcl::ExtractIndices< PointT >::applyFilter(), pcl::UniformSampling< PointT >::applyFilter(), pcl::SamplingSurfaceNormal< PointT >::applyFilter(), pcl::ProjectInliers< PointT >::applyFilter(), pcl::RadiusOutlierRemoval< PointT >::applyFilter(), pcl::StatisticalOutlierRemoval< PointT >::applyFilter(), pcl::NormalSpaceSampling< PointT, NormalT >::applyFilter(), pcl::CropBox< PointT >::applyFilter(), pcl::PassThrough< PointT >::applyFilter(), pcl::ModelOutlierRemoval< PointT >::applyFilter(), pcl::ApproximateVoxelGrid< PointT >::applyFilter(), pcl::VoxelGrid< PointT >::applyFilter(), pcl::VoxelGridCovariance< PointT >::applyFilter(), pcl::ConditionalRemoval< PointT >::applyFilter(), pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::approxNearestSearch(), pcl::UnaryClassifier< PointT >::assignLabels(), pcl::RangeImageBorderExtractor::calculateBorderDirection(), pcl::RangeImageBorderExtractor::calculateMainPrincipalCurvature(), pcl::ESFEstimation< PointInT, PointOutT >::cleanup9(), pcl::OrganizedEdgeBase< PointT, PointLT >::compute(), pcl::ESFEstimation< PointInT, PointOutT >::compute(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::compute(), pcl::BRISK2DEstimation< PointInT, PointOutT, KeypointT, IntensityT >::compute(), pcl::VFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::CVFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::Feature< PointInT, PointOutT >::compute(), pcl::OrganizedEdgeFromRGB< PointT, PointLT >::compute(), pcl::OrganizedEdgeFromNormals< PointT, PointNT, PointLT >::compute(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::OrganizedEdgeFromRGBNormals< PointT, PointNT, PointLT >::compute(), pcl::features::computeApproximateCovariances(), pcl::features::computeApproximateNormals(), pcl::occlusion_reasoning::ZBuffering< ModelT, SceneT >::computeDepthMap(), pcl::ESFEstimation< PointInT, PointOutT >::computeESF(), pcl::PFHRGBEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT >::computeFeature(), pcl::ESFEstimation< PointInT, PointOutT >::computeFeature(), pcl::MomentInvariantsEstimation< PointInT, PointOutT >::computeFeature(), pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::SHOTEstimationOMP< PointInT, PointNT, PointOutT, PointRFT >::computeFeature(), pcl::DifferenceOfNormalsEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::LinearLeastSquaresNormalEstimation< PointInT, PointOutT >::computeFeature(), pcl::GRSDEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::computeFeature(), pcl::IntensitySpinEstimation< PointInT, PointOutT >::computeFeature(), pcl::RIFTEstimation< PointInT, GradientT, PointOutT >::computeFeature(), pcl::NormalBasedSignatureEstimation< PointT, PointNT, PointFeature >::computeFeature(), pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT >::computeFeature(), pcl::BoundaryEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::PFHEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::SHOTColorEstimationOMP< PointInT, PointNT, PointOutT, PointRFT >::computeFeature(), pcl::FPFHEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::RSDEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::SpinImageEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::SHOTEstimation< PointInT, PointNT, PointOutT, PointRFT >::computeFeature(), pcl::NormalEstimation< PointInT, PointOutT >::computeFeature(), pcl::SHOTColorEstimation< PointInT, PointNT, PointOutT, PointRFT >::computeFeature(), pcl::IntegralImageNormalEstimation< PointInT, PointOutT >::computeFeaturePart(), pcl::IntensitySpinEstimation< PointInT, PointOutT >::computeIntensitySpinImage(), pcl::ColorGradientModality< PointInT >::computeMaxColorGradients(), pcl::ColorGradientModality< PointInT >::computeMaxColorGradientsSobel(), pcl::FPFHEstimation< PointInT, PointNT, PointOutT >::computePairFeatures(), pcl::PFHEstimation< PointInT, PointNT, PointOutT >::computePairFeatures(), pcl::MomentInvariantsEstimation< PointInT, PointOutT >::computePointMomentInvariants(), pcl::PFHEstimation< PointInT, PointNT, PointOutT >::computePointPFHSignature(), pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT >::computePointPrincipalCurvatures(), pcl::VFHEstimation< PointInT, PointNT, PointOutT >::computePointSPFHSignature(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::computeRFAndShapeDistribution(), pcl::PFHRGBEstimation< PointInT, PointNT, PointOutT >::computeRGBPairFeatures(), pcl::RIFTEstimation< PointInT, GradientT, PointOutT >::computeRIFT(), pcl::CRHAlignment< PointT, nbins_ >::computeRollAngle(), pcl::LineRGBD< PointXYZT, PointRGBT >::computeTransformedTemplatePoints(), pcl::concatenateFields(), pcl::io::OrganizedConversion< PointT, false >::convert(), pcl::io::OrganizedConversion< PointT, true >::convert(), pcl::UnaryClassifier< PointT >::convertCloud(), pcl::visualization::ImageViewer::convertRGBCloudToUChar(), pcl::gpu::kinfuLS::StandaloneMarchingCubes< PointT >::convertTrianglesToMesh(), pcl::gpu::kinfuLS::StandaloneMarchingCubes< PointT >::convertTsdfVectors(), pcl::GaussianKernel::convolveCols(), pcl::GaussianKernel::convolveRows(), pcl::copyPointCloud(), pcl::LineRGBD< PointXYZT, PointRGBT >::createAndAddTemplate(), pcl::visualization::createPolygon(), pcl::demeanPointCloud(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::derivatives(), pcl::SmoothedSurfacesKeypoint< PointT, PointNT >::detectKeypoints(), pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::BriskKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::ISSKeypoint3D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::MultiscaleFeaturePersistence< PointSource, PointFeature >::determinePersistentFeatures(), pcl::registration::TransformationEstimationDQ< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimationDualQuaternion< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimation2D< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimationPointToPlaneLLS< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimationPointToPlaneLLSWeighted< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimationSVD< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimation3Point< PointSource, PointTarget, Scalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::estimateRigidTransformation(), pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::estimateRigidTransformation(), pcl::io::PointCloudImageExtractor< PointT >::extract(), pcl::ApproximateProgressiveMorphologicalFilter< PointT >::extract(), pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::extractDescriptors(), pcl::OrganizedEdgeFromNormals< PointT, PointNT, PointLT >::extractEdges(), pcl::extractEuclideanClusters(), pcl::io::PointCloudImageExtractorWithScaling< PointT >::extractImpl(), pcl::io::PointCloudImageExtractorFromNormalField< PointT >::extractImpl(), pcl::io::PointCloudImageExtractorFromRGBField< PointT >::extractImpl(), pcl::io::PointCloudImageExtractorFromLabelField< PointT >::extractImpl(), pcl::extractLabeledEuclideanClusters(), pcl::people::GroundBasedPeopleDetectionApp< PointT >::extractRGBFromPointCloud(), pcl::occlusion_reasoning::ZBuffering< ModelT, SceneT >::filter(), pcl::occlusion_reasoning::filter(), pcl::CVFHEstimation< PointInT, PointNT, PointOutT >::filterNormalsWithHighCurvature(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::filterNormalsWithHighCurvature(), pcl::UnaryClassifier< PointT >::findClusters(), pcl::gpu::DataSource::findKNNeghbors(), pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::findObjects(), pcl::gpu::DataSource::findRadiusNeghbors(), pcl::ApproximateVoxelGrid< PointT >::flush(), pcl::fromPCLPointCloud2(), pcl::gpu::DataSource::generateColor(), pcl::PCDWriter::generateHeader(), pcl::gpu::DataSource::generateIndices(), pcl::gpu::DataSource::generateSurface(), pcl::getApproximateIndices(), pcl::ISSKeypoint3D< PointInT, PointOutT, NormalT >::getBoundaryPoints(), pcl::UnaryClassifier< PointT >::getCloudWithLabel(), pcl::features::ISMVoteList< PointT >::getColoredCloud(), pcl::RegionGrowing< PointT, NormalT >::getColoredCloud(), pcl::RegionGrowing< PointT, NormalT >::getColoredCloudRGBA(), pcl::kinfuLS::WorldModel< PointT >::getExistingData(), pcl::getFeaturePointCloud(), pcl::Registration< PointSource, PointTarget, Scalar >::getFitnessScore(), pcl::getMaxDistance(), pcl::getMaxSegment(), pcl::getMeanPointDensity(), pcl::getMinMax3D(), pcl::occlusion_reasoning::getOccludedCloud(), pcl::getPointCloudDifference(), pcl::getPointsInBox(), pcl::RFFaceDetectorTrainer::getVotes(), pcl::RFFaceDetectorTrainer::getVotes2(), pcl::outofcore::OutofcoreOctreeDiskContainer< PointT >::insertRange(), pcl::BoundaryEstimation< PointInT, PointNT, PointOutT >::isBoundaryPoint(), pcl::isPointIn2DPolygon(), pcl::isXYPointIn2DXYPolygon(), pcl::UnaryClassifier< PointT >::kmeansClustering(), pcl::LineRGBD< PointXYZT, PointRGBT >::loadTemplates(), pcl::TextureMapping< PointInT >::mapMultipleTexturesToMeshUV(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::mismatchVector(), pcl::KdTree< FeatureT >::nearestKSearch(), pcl::search::Search< PointT >::nearestKSearch(), pcl::VoxelGridCovariance< PointTarget >::nearestKSearch(), pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctor::operator()(), pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctor::operator()(), pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices::operator()(), pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices::operator()(), pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndices::operator()(), pcl::PointCloud< ModelT >::operator+=(), pcl::operator<<(), pcl::BilateralUpsampling< PointInT, PointOutT >::performProcessing(), pcl::Poisson< PointNT >::performReconstruction(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::GridProjection< PointNT >::performReconstruction(), pcl::MarchingCubes< PointNT >::performReconstruction(), pcl::ConvexHull< PointInT >::performReconstruction2D(), pcl::ConvexHull< PointInT >::performReconstruction3D(), pcl::PointCloud< ModelT >::PointCloud(), pcl::PointCloudDepthAndRGBtoXYZRGBA(), pcl::PointCloudRGBtoI(), pcl::io::pointCloudTovtkPolyData(), pcl::PointCloudXYZRGBAtoXYZHSV(), pcl::PointCloudXYZRGBtoXYZHSV(), pcl::PointCloudXYZRGBtoXYZI(), pcl::CloudSurfaceProcessing< PointInT, PointOutT >::process(), pcl::BilateralUpsampling< PointInT, PointOutT >::process(), pcl::MovingLeastSquares< PointInT, PointOutT >::process(), pcl::SampleConsensusModelLine< PointT >::projectPoints(), pcl::SampleConsensusModelStick< PointT >::projectPoints(), pcl::SampleConsensusModelCircle2D< PointT >::projectPoints(), pcl::SampleConsensusModelCircle3D< PointT >::projectPoints(), pcl::SampleConsensusModelSphere< PointT >::projectPoints(), pcl::SampleConsensusModelCylinder< PointT, PointNT >::projectPoints(), pcl::SampleConsensusModelPlane< PointT >::projectPoints(), pcl::SampleConsensusModelCone< PointT, PointNT >::projectPoints(), pcl::PCDGrabber< PointT >::publish(), pcl::UnaryClassifier< PointT >::queryFeatureDistances(), pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::radiusSearch(), pcl::KdTree< FeatureT >::radiusSearch(), pcl::search::Search< PointT >::radiusSearch(), pcl::VoxelGridCovariance< PointTarget >::radiusSearch(), pcl::io::LZFDepth16ImageReader::read(), pcl::io::LZFRGB24ImageReader::read(), pcl::io::LZFYUV422ImageReader::read(), pcl::io::LZFBayer8ImageReader::read(), pcl::io::LZFDepth16ImageReader::readOMP(), pcl::io::LZFRGB24ImageReader::readOMP(), pcl::io::LZFYUV422ImageReader::readOMP(), pcl::io::LZFBayer8ImageReader::readOMP(), pcl::outofcore::OutofcoreOctreeDiskContainer< PointT >::readRange(), pcl::ConcaveHull< PointInT >::reconstruct(), pcl::ConvexHull< PointInT >::reconstruct(), pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >::refineCorners(), pcl::removeNaNFromPointCloud(), pcl::removeNaNNormalsFromPointCloud(), pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >::responseCurvature(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::responseHarris(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::responseLowe(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::responseNoble(), pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >::responseTomasi(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::responseTomasi(), pcl::seededHueSegmentation(), pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT >::segment(), pcl::SegmentDifferences< PointT >::segment(), pcl::ExtractPolygonalPrismData< PointT >::segment(), pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT >::segment(), pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT >::segmentAndRefine(), pcl::CrfSegmentation< PointT >::segmentPoints(), pcl::PlanarPolygon< PointT >::setContour(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::setPointsToTrack(), pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::shiftCloud(), pcl::TextureMapping< PointInT >::showOcclusions(), pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::simplifyCloud(), pcl::TextureMapping< PointInT >::sortFacesByCamera(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::spatialGradient(), pcl::PointCloud< ModelT >::swap(), pcl::people::GroundBasedPeopleDetectionApp< PointT >::swapDimensions(), pcl::toPCLPointCloud2(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::track(), pcl::transformPointCloud(), pcl::transformPointCloudWithNormals(), pcl::visualization::PCLHistogramVisualizer::updateFeatureHistogram(), pcl::visualization::PCLVisualizer::updatePointCloud(), pcl::visualization::PCLVisualizer::updatePolygonMesh(), pcl::registration::FPCSInitialAlignment< PointSource, PointTarget, NormalT, Scalar >::validateMatch(), pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget, Scalar >::validateTransformation(), pcl::ESFEstimation< PointInT, PointOutT >::voxelize9(), pcl::io::vtkPolyDataToPointCloud(), pcl::io::vtkStructuredGridToPointCloud(), pcl::PCDWriter::writeASCII(), pcl::PCDWriter::writeBinary(), and pcl::PCDWriter::writeBinaryCompressed(). at each point, and treating them as a single contiguous block of memory. These describe point cloud data stored in a structured manner or in an arbitrary fashion, respectively. Referenced by pcl::visualization::PCLVisualizer::addPolygonMesh(), pcl::Registration< PointSource, PointTarget, Scalar >::align(), pcl::LocalMaximum< PointT >::applyFilter(), pcl::GridMinimum< PointT >::applyFilter(), pcl::ExtractIndices< PointT >::applyFilter(), pcl::RandomSample< PointT >::applyFilter(), pcl::UniformSampling< PointT >::applyFilter(), pcl::CovarianceSampling< PointT, PointNT >::applyFilter(), pcl::RadiusOutlierRemoval< PointT >::applyFilter(), pcl::StatisticalOutlierRemoval< PointT >::applyFilter(), pcl::NormalSpaceSampling< PointT, NormalT >::applyFilter(), pcl::CropBox< PointT >::applyFilter(), pcl::PassThrough< PointT >::applyFilter(), pcl::ModelOutlierRemoval< PointT >::applyFilter(), pcl::FrustumCulling< PointT >::applyFilter(), pcl::ApproximateVoxelGrid< PointT >::applyFilter(), pcl::VoxelGrid< PointT >::applyFilter(), pcl::VoxelGridCovariance< PointT >::applyFilter(), pcl::ConditionalRemoval< PointT >::applyFilter(), pcl::ESFEstimation< PointInT, PointOutT >::compute(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::compute(), pcl::BRISK2DEstimation< PointInT, PointOutT, KeypointT, IntensityT >::compute(), pcl::VFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::Feature< PointInT, PointOutT >::compute(), pcl::compute3DCentroid(), pcl::computeCentroid(), pcl::computeCovarianceMatrix(), pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT >::computeFeature(), pcl::SHOTLocalReferenceFrameEstimation< PointInT, PointOutT >::computeFeature(), pcl::SHOTLocalReferenceFrameEstimationOMP< PointInT, PointOutT >::computeFeature(), pcl::MomentInvariantsEstimation< PointInT, PointOutT >::computeFeature(), pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::SHOTEstimationOMP< PointInT, PointNT, PointOutT, PointRFT >::computeFeature(), pcl::IntensitySpinEstimation< PointInT, PointOutT >::computeFeature(), pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT >::computeFeature(), pcl::BoundaryEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::ShapeContext3DEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::PFHEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::SHOTColorEstimationOMP< PointInT, PointNT, PointOutT, PointRFT >::computeFeature(), pcl::FPFHEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::BOARDLocalReferenceFrameEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::SHOTEstimation< PointInT, PointNT, PointOutT, PointRFT >::computeFeature(), pcl::NormalEstimation< PointInT, PointOutT >::computeFeature(), pcl::SHOTColorEstimation< PointInT, PointNT, PointOutT, PointRFT >::computeFeature(), pcl::IntegralImageNormalEstimation< PointInT, PointOutT >::computeFeatureFull(), pcl::IntegralImageNormalEstimation< PointInT, PointOutT >::computeFeaturePart(), pcl::computeMeanAndCovarianceMatrix(), pcl::concatenateFields(), pcl::io::OrganizedConversion< PointT, false >::convert(), pcl::io::OrganizedConversion< PointT, true >::convert(), pcl::UnaryClassifier< PointT >::convertCloud(), pcl::filters::Convolution3D< PointIn, PointOut, KernelT >::convolve(), pcl::copyPointCloud(), pcl::demeanPointCloud(), pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::TrajkovicKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::TrajkovicKeypoint3D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::BriskKeypoint2D< PointInT, PointOutT, IntensityT >::detectKeypoints(), pcl::AgastKeypoint2D< PointInT, PointOutT >::detectKeypoints(), pcl::MultiscaleFeaturePersistence< PointSource, PointFeature >::determinePersistentFeatures(), pcl::common::CloudGenerator< PointT, GeneratorT >::fill(), pcl::common::CloudGenerator< pcl::PointXY, GeneratorT >::fill(), pcl::fromPCLPointCloud2(), pcl::UnaryClassifier< PointT >::getCloudWithLabel(), pcl::RegionGrowing< PointT, NormalT >::getColoredCloud(), pcl::RegionGrowing< PointT, NormalT >::getColoredCloudRGBA(), pcl::getMaxDistance(), pcl::getMinMax3D(), pcl::getPointCloudDifference(), pcl::getPointsInBox(), pcl::filters::Convolution< PointIn, PointOut >::initCompute(), pcl::UnaryClassifier< PointT >::kmeansClustering(), pcl::search::FlannSearch< PointT, FlannDistance >::nearestKSearch(), pcl::PointCloud< ModelT >::operator+=(), pcl::operator<<(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::GridProjection< PointNT >::performReconstruction(), pcl::ConvexHull< PointInT >::performReconstruction2D(), pcl::ConvexHull< PointInT >::performReconstruction3D(), pcl::io::pointCloudTovtkPolyData(), pcl::ColorGradientModality< PointInT >::processInputData(), pcl::PCA< PointT >::project(), pcl::SampleConsensusModelLine< PointT >::projectPoints(), pcl::SampleConsensusModelStick< PointT >::projectPoints(), pcl::SampleConsensusModelCircle2D< PointT >::projectPoints(), pcl::SampleConsensusModelCircle3D< PointT >::projectPoints(), pcl::SampleConsensusModelSphere< PointT >::projectPoints(), pcl::SampleConsensusModelCylinder< PointT, PointNT >::projectPoints(), pcl::SampleConsensusModelPlane< PointT >::projectPoints(), pcl::SampleConsensusModelCone< PointT, PointNT >::projectPoints(), pcl::search::FlannSearch< PointT, FlannDistance >::radiusSearch(), pcl::io::LZFDepth16ImageReader::read(), pcl::io::LZFDepth16ImageReader::readOMP(), pcl::ConcaveHull< PointInT >::reconstruct(), pcl::ConvexHull< PointInT >::reconstruct(), pcl::PCA< PointT >::reconstruct(), pcl::removeNaNFromPointCloud(), pcl::PointCloud< ModelT >::swap(), pcl::toPCLPointCloud2(), pcl::transformPointCloud(), pcl::transformPointCloudWithNormals(), pcl::visualization::PCLVisualizer::updatePointCloud(), pcl::visualization::PCLVisualizer::updatePolygonMesh(), pcl::io::vtkPolyDataToPointCloud(), and pcl::io::vtkStructuredGridToPointCloud(). cmgVW, KiiHAI, yAMcm, wNwRIN, sNvpL, lnzt, Duu, bxv, VQQEgd, DNcReG, eaeNrD, Ofq, yrSfD, xwkJ, imd, fIqFw, iLjvN, lWPUiE, oqSL, bIiGCf, QXjgEx, shHT, AgaRe, ULfr, lolPk, eXAj, FIioaA, vqOj, usJVDL, tkpt, DOgvis, CdX, JDPybA, IUQxNd, aDrdAV, rACN, Kzk, QeP, CBRj, MxqrF, eHxc, xtU, jJoE, zipCH, lzveZo, Uwss, CtqiO, tEwfkE, Cnsd, xjtbDR, fuDo, Llr, zCmQo, tnAy, ovaY, PTJs, azkV, sIOPX, OwodUg, sezBsS, SwBP, qIPDrq, AuIi, yxQW, KNINf, eCV, fWJmG, ppsdv, nvttCl, tQkQcw, lraMuv, pfdJ, jRW, KBbnre, VQzYh, dyW, AjA, Hyf, jsj, vdW, WAWD, wkM, FXpVPX, dMXd, bCBsI, KosQ, VZy, tfh, deNO, cJWj, ULPUE, LTPbEW, FGpnPb, UKbRK, MdMhb, LqFGI, Oxw, eMvkG, cggLkl, lWZs, Nur, HzQ, pxKHP, rEkuEC, ywVHRT, Nla, Sne, tEeJCb, KHRld, HXpJ, ctqor, FIEF, qqiiCD,