├──── pcl.find_library(name)
None
├──── pcl.save(cloud, path, format=None, binary=False)
Save pointcloud to file.
    Format should be "pcd", "ply", or None to infer from the pathname.
   
├──── pcl._infer_format(path, format)
None
├──── pcl._encode(path)
None
├──── pcl.load_XYZRGBA(path, format=None)

    Load pointcloud from path.
    Currently supports PCD and PLY files.
    Format should be "pcd", "ply", "obj", or None to infer from the pathname.
   
├──── pcl.save_PointNormal(cloud, path, format=None, binary=False)

    Save pointcloud to file.
    Format should be "pcd", "ply", or None to infer from the pathname.
   
├──── pcl.load_XYZI(path, format=None)
Load pointcloud from path.
    Currently supports PCD and PLY files.
    Format should be "pcd", "ply", "obj", or None to infer from the pathname.
   
├──── pcl.load_PointWithViewpoint(path, format=None)

    Load pointcloud from path.
    Currently supports PCD and PLY files.
    Format should be "pcd", "ply", "obj", or None to infer from the pathname.
   
├──── pcl.load_XYZRGB(path, format=None)

    Load pointcloud from path.
    Currently supports PCD and PLY files.
    Format should be "pcd", "ply", "obj", or None to infer from the pathname.
   
├──── pcl.save_XYZRGBA(cloud, path, format=None, binary=False)
Save pointcloud to file.
    Format should be "pcd", "ply", or None to infer from the pathname.
   
├──── pcl.load(path, format=None)
Load pointcloud from path.

    Currently supports PCD and PLY files.
    Format should be "pcd", "ply", "obj", or None to infer from the pathname.
   
├──── pcl.RadiusOutlierRemoval
   └──── set_radius_search(self, double radius)
   └──── set_MinNeighborsInRadius(self, int min_pts)
   └──── get_radius_search(self)
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── get_MinNeighborsInRadius(self)
├──── pcl.SampleConsensusModelRegistration
├──── pcl.OctreePointCloudSearch_PointXYZRGBA
   └──── define_bounding_box(self)

        Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
   └──── add_points_from_input_cloud(self)

        Add points from input point cloud to octree
   └──── is_voxel_occupied_at_point(self, point)

        Check if voxel at given point coordinates exist
   └──── delete_voxel_at_point(self, point)

        Delete leaf node / voxel at given point
   └──── get_occupied_voxel_centers(self)

        Get list of centers of all occupied voxels
   └──── radius_search(self, point, double radius, unsigned int max_nn=0)

        Search for all neighbors of query point that are within a given radius
├──── pcl.Segmentation
   └──── set_method_type(self, int m)
   └──── set_distance_threshold(self, float d)
   └──── set_MaxIterations(self, int count)
   └──── set_model_type(self, SacModel m)
   └──── segment(self)
   └──── set_optimize_coefficients(self, bool b)
├──── pcl.KdTreeFLANN_PointXYZI
   └──── nearest_k_search_for_point(self, PointCloud_PointXYZI pc, int index, int k=1)

        Find the k nearest neighbours and squared distances for the point
        at pc[index]
   └──── nearest_k_search_for_cloud(self, PointCloud_PointXYZI pc, int k=1)

        Find the k nearest neighbours and squared distances for all points
        in the pointcloud
├──── pcl.StatisticalOutlierRemovalFilter_PointXYZRGB
   └──── set_mean_k(self, int k)

        Set the number of points (k) to use for mean distance estimation
   └──── set_negative(self, bool negative)

        Set whether the indices should be returned, or all points except the indices
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_InputCloud(self, PointCloud_PointXYZRGB pc)
   └──── set_std_dev_mul_thresh(self, double std_mul)

        Set the standard deviation multiplier threshold
├──── pcl.ConcaveHull_PointXYZRGBA
   └──── set_Alpha(self, double d)
   └──── reconstruct(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
├──── pcl.Segmentation_PointXYZI
   └──── set_method_type(self, int m)
   └──── set_distance_threshold(self, float d)
   └──── set_model_type(self, SacModel m)
   └──── segment(self)
   └──── set_optimize_coefficients(self, bool b)
├──── pcl.NormalEstimation
   └──── compute(self)
   └──── set_SearchMethod(self, KdTree kdtree)
   └──── set_RadiusSearch(self, double param)
   └──── set_KSearch(self, int param)
├──── pcl.RandomSampleConsensus
   └──── set_DistanceThreshold(self, double param)
   └──── get_Inliers(self)
   └──── computeModel(self)
├──── pcl.PointCloud_PointXYZRGB
   └──── from_list(self, _list)

        Fill this pointcloud from a list of 4-tuples
       
   └──── __reduce__(self)
   └──── from_array(self, ndarray arr)

        Fill this object from a 2D numpy array (float32)
       
   └──── _to_ply_file(self, const char *f, bool binary=False)
   └──── to_array(self)

        Return this object as a 2D numpy array (float32)
       
   └──── extract(self, pyindices, bool negative=False)

        Given a list of indices of points in the pointcloud, return a
        new pointcloud containing only those points
   └──── make_voxel_grid_filter(self)

        Return a pcl
   └──── make_segmenter(self)

        Return a pcl
   └──── _from_obj_file(self, const char *s)
   └──── to_file(self, const char *fname, bool ascii=True)
Save pointcloud to a file in PCD format
   └──── make_passthrough_filter(self)

        Return a pcl
   └──── _from_ply_file(self, const char *s)
   └──── make_kdtree_flann(self)

        Return a pcl
   └──── _from_pcd_file(self, const char *s)
   └──── resize(self, npy_intp x)
   └──── make_statistical_outlier_filter(self)

        Return a pcl
   └──── from_file(self, char *f)

        Fill this pointcloud from a file (a local path)
   └──── _to_pcd_file(self, const char *f, bool binary=False)
   └──── to_list(self)

        Return this object as a list of 4-tuples
       
   └──── get_point(self, npy_intp row, npy_intp col)

        Return a point (3-tuple) at the given row/column
       
   └──── make_moving_least_squares(self)

        Return a pcl
   └──── make_segmenter_normals(self, int ksearch=-1, double searchRadius=-1
├──── pcl.OctreePointCloud2Buf_PointXYZRGB
   └──── set_input_cloud(self, PointCloud_PointXYZRGB pc)

        Provide a pointer to the input data set
   └──── delete_tree(self)

        Delete the octree structure and its leaf nodes
├──── pcl.ConcaveHull
   └──── set_Alpha(self, double d)
   └──── reconstruct(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
├──── pcl.OctreePointCloud_PointXYZI
   └──── set_input_cloud(self, PointCloud_PointXYZI pc)

        Provide a pointer to the input data set
   └──── delete_tree(self)

        Delete the octree structure and its leaf nodes
├──── pcl.ConditionalRemoval
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_KeepOrganized(self, flag)
├──── pcl.PointCloud_Normal
   └──── from_list(self, _list)

        Fill this pointcloud from a list of 4-tuples
       
   └──── __reduce__(self)
   └──── to_list(self)

        Return this object as a list of 4-tuples
       
   └──── get_point(self, npy_intp row, npy_intp col)

        Return a point (4-tuple) at the given row/column
       
   └──── from_array(self, ndarray arr)

        Fill this object from a 2D numpy array (float32)
       
   └──── to_array(self)

        Return this object as a 2D numpy array (float32)
       
   └──── resize(self, npy_intp x)
├──── pcl.KdTreeFLANN_PointXYZRGB
   └──── nearest_k_search_for_point(self, PointCloud_PointXYZRGB pc, int index, int k=1)

        Find the k nearest neighbours and squared distances for the point
        at pc[index]
   └──── nearest_k_search_for_cloud(self, PointCloud_PointXYZRGB pc, int k=1)

        Find the k nearest neighbours and squared distances for all points
        in the pointcloud
├──── pcl.OctreePointCloud2Buf
   └──── set_input_cloud(self, PointCloud pc)

        Provide a pointer to the input data set
   └──── delete_tree(self)

        Delete the octree structure and its leaf nodes
├──── pcl.Vertices
   └──── from_list(self, _list)

        Fill this pointcloud from a list of 3-tuples
       
   └──── to_list(self)

        Return this object as a list of 3-tuples
       
   └──── from_array(self, ndarray arr)

        Fill this object from a 2D numpy array (float32)
       
   └──── to_array(self)

        Return this object as a 2D numpy array (float32)
       
   └──── resize(self, npy_intp x)
├──── pcl.PointCloud_PointXYZI
   └──── from_list(self, _list)

        Fill this pointcloud from a list of 4-tuples
       
   └──── __reduce__(self)
   └──── from_array(self, ndarray arr)

        Fill this object from a 2D numpy array (float32)
       
   └──── _to_ply_file(self, const char *f, bool binary=False)
   └──── to_array(self)

        Return this object as a 2D numpy array (float32)
       
   └──── extract(self, pyindices, bool negative=False)

        Given a list of indices of points in the pointcloud, return a
        new pointcloud containing only those points
   └──── make_voxel_grid_filter(self)

        Return a pcl
   └──── make_segmenter(self)

        Return a pcl
   └──── _from_obj_file(self, const char *s)
   └──── to_file(self, const char *fname, bool ascii=True)
Save pointcloud to a file in PCD format
   └──── make_passthrough_filter(self)

        Return a pcl
   └──── _from_ply_file(self, const char *s)
   └──── make_kdtree_flann(self)

        Return a pcl
   └──── _from_pcd_file(self, const char *s)
   └──── resize(self, npy_intp x)
   └──── make_statistical_outlier_filter(self)

        Return a pcl
   └──── from_file(self, char *f)

        Fill this pointcloud from a file (a local path)
   └──── _to_pcd_file(self, const char *f, bool binary=False)
   └──── to_list(self)

        Return this object as a list of 4-tuples
       
   └──── get_point(self, npy_intp row, npy_intp col)

        Return a point (4-tuple) at the given row/column
       
   └──── make_segmenter_normals(self, int ksearch=-1, double searchRadius=-1
├──── pcl.SampleConsensusModelStick
├──── pcl.StatisticalOutlierRemovalFilter_PointXYZI
   └──── set_mean_k(self, int k)

        Set the number of points (k) to use for mean distance estimation
   └──── set_negative(self, bool negative)

        Set whether the indices should be returned, or all points except the indices
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_InputCloud(self, PointCloud_PointXYZI pc)
   └──── set_std_dev_mul_thresh(self, double std_mul)

        Set the standard deviation multiplier threshold
├──── pcl.OctreePointCloud
   └──── set_input_cloud(self, PointCloud pc)

        Provide a pointer to the input data set
   └──── delete_tree(self)

        Delete the octree structure and its leaf nodes
├──── pcl.RegionGrowing
   └──── get_SmoothModeFlag(self)
   └──── get_ResidualThreshold(self)
   └──── get_MaxClusterSize(self)
   └──── get_ResidualTestFlag(self)
   └──── get_MinClusterSize(self)
   └──── set_MinClusterSize(self, int min)
   └──── set_CurvatureThreshold(self, float curvature)
   └──── get_CurvatureTestFlag(self)
   └──── get_SmoothnessThreshold(self)
   └──── get_SegmentFromPoint(self, int index)
   └──── set_MaxClusterSize(self, int max)
   └──── set_ResidualThreshold(self, float residual)
   └──── set_SmoothModeFlag(self, bool value)
   └──── set_SearchMethod(self, KdTree kdtree)
   └──── set_ResidualTestFlag(self, bool value)
   └──── set_InputNormals(self, PointCloud_Normal normals)
   └──── get_CurvatureThreshold(self)
   └──── set_SmoothnessThreshold(self, float theta)
   └──── set_NumberOfNeighbours(self, int neighbour_number)
   └──── set_CurvatureTestFlag(self, bool value)
   └──── Extract(self)
   └──── get_NumberOfNeighbours(self)
├──── pcl.OctreePointCloudChangeDetector_PointXYZRGBA
   └──── define_bounding_box(self)

        Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
   └──── add_points_from_input_cloud(self)

        Add points from input point cloud to octree
   └──── is_voxel_occupied_at_point(self, point)

        Check if voxel at given point coordinates exist
   └──── get_PointIndicesFromNewVoxels(self)
   └──── delete_voxel_at_point(self, point)

        Delete leaf node / voxel at given point
   └──── get_occupied_voxel_centers(self)

        Get list of centers of all occupied voxels
├──── pcl.EuclideanClusterExtraction
   └──── set_ClusterTolerance(self, double b)
   └──── set_SearchMethod(self, KdTree kdtree)
   └──── set_MinClusterSize(self, int min)
   └──── set_MaxClusterSize(self, int max)
   └──── Extract(self)
├──── pcl.VoxelGridFilter
   └──── set_leaf_size(self, float x, float y, float z)

        Set the voxel grid leaf size
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
├──── pcl.OctreePointCloud_PointXYZRGB
   └──── set_input_cloud(self, PointCloud_PointXYZRGB pc)

        Provide a pointer to the input data set
   └──── delete_tree(self)

        Delete the octree structure and its leaf nodes
├──── pcl.OctreePointCloud2Buf_PointXYZRGBA
   └──── set_input_cloud(self, PointCloud_PointXYZRGBA pc)

        Provide a pointer to the input data set
   └──── delete_tree(self)

        Delete the octree structure and its leaf nodes
├──── pcl.Segmentation_PointXYZRGB
   └──── set_method_type(self, int m)
   └──── set_distance_threshold(self, float d)
   └──── set_model_type(self, SacModel m)
   └──── segment(self)
   └──── set_optimize_coefficients(self, bool b)
├──── pcl.Segmentation_PointXYZRGBA_Normal
   └──── set_method_type(self, int m)
   └──── get_eps_angle(self)
   └──── get_min_max_opening_angle(self)
   └──── set_distance_threshold(self, float d)
   └──── set_axis(self, double ax1, double ax2, double ax3)
   └──── set_radius_limits(self, float f1, float f2)
   └──── get_axis(self)
   └──── set_max_iterations(self, int i)
   └──── set_min_max_opening_angle(self, double min_angle, double max_angle)

        Set the minimum and maximum cone opening angles in radians for a cone model
   └──── set_eps_angle(self, double ea)
   └──── set_normal_distance_weight(self, float f)
   └──── set_model_type(self, SacModel m)
   └──── segment(self)
   └──── set_optimize_coefficients(self, bool b)
├──── pcl.StatisticalOutlierRemovalFilter
   └──── set_mean_k(self, int k)

        Set the number of points (k) to use for mean distance estimation
   └──── set_negative(self, bool negative)

        Set whether the indices should be returned, or all points except the indices
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_InputCloud(self, PointCloud pc)
   └──── set_std_dev_mul_thresh(self, double std_mul)

        Set the standard deviation multiplier threshold
├──── pcl.SampleConsensusModelLine
├──── pcl.VoxelGridFilter_PointXYZI
   └──── set_leaf_size(self, float x, float y, float z)

        Set the voxel grid leaf size
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
├──── pcl.PassThroughFilter_PointXYZRGBA
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new PointCloud_PointXYZRGBA
       
   └──── set_filter_limits(self, float filter_min, float filter_max)
   └──── set_filter_field_name(self, field_name)
├──── pcl.OctreePointCloudSearch_PointXYZI
   └──── define_bounding_box(self)

        Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
   └──── add_points_from_input_cloud(self)

        Add points from input point cloud to octree
   └──── is_voxel_occupied_at_point(self, point)

        Check if voxel at given point coordinates exist
   └──── delete_voxel_at_point(self, point)

        Delete leaf node / voxel at given point
   └──── get_occupied_voxel_centers(self)

        Get list of centers of all occupied voxels
   └──── radius_search(self, point, double radius, unsigned int max_nn=0)

        Search for all neighbors of query point that are within a given radius
├──── pcl.MovingLeastSquares_PointXYZRGB
   └──── set_polynomial_fit(self, bool fit)

        Sets whether the surface and normal are approximated using a polynomial,
        or only via tangent estimation
   └──── set_polynomial_order(self, bool order)

        Set the order of the polynomial to be fit
   └──── set_search_radius(self, double radius)

        Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting
   └──── process(self)

        Apply the smoothing according to the previously set values and return
        a new pointcloud
       
├──── pcl.PointCloud_PointWithViewpoint
   └──── from_file(self, char *f)

        Fill this pointcloud from a file (a local path)
   └──── from_list(self, _list)

        Fill this pointcloud from a list of 6-tuples
       
   └──── __reduce__(self)
   └──── to_list(self)

        Return this object as a list of 6-tuples
       
   └──── get_point(self, npy_intp row, npy_intp col)

        Return a point (6-tuple) at the given row/column
       
   └──── from_array(self, ndarray arr)

        Fill this object from a 2D numpy array (float32)
       
   └──── _from_ply_file(self, const char *s)
   └──── to_array(self)

        Return this object as a 2D numpy array (float32)
       
   └──── to_file(self, const char *fname, bool ascii=True)

        Save pointcloud to a file in PCD format
   └──── _from_pcd_file(self, const char *s)
   └──── resize(self, npy_intp x)
├──── pcl.SegmentationNormal
   └──── set_method_type(self, int m)
   └──── get_eps_angle(self)
   └──── get_min_max_opening_angle(self)
   └──── set_distance_threshold(self, float d)
   └──── set_axis(self, double ax1, double ax2, double ax3)
   └──── set_radius_limits(self, float f1, float f2)
   └──── get_axis(self)
   └──── set_max_iterations(self, int i)
   └──── set_min_max_opening_angle(self, double min_angle, double max_angle)

        Set the minimum and maximum cone opening angles in radians for a cone model
   └──── set_eps_angle(self, double ea)
   └──── set_normal_distance_weight(self, float f)
   └──── set_model_type(self, SacModel m)
   └──── segment(self)
   └──── set_optimize_coefficients(self, bool b)
├──── pcl.OctreePointCloud_PointXYZRGBA
   └──── set_input_cloud(self, PointCloud_PointXYZRGBA pc)

        Provide a pointer to the input data set
   └──── delete_tree(self)

        Delete the octree structure and its leaf nodes
├──── pcl.VoxelGridFilter_PointXYZRGB
   └──── set_leaf_size(self, float x, float y, float z)

        Set the voxel grid leaf size
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
├──── pcl.OctreePointCloudSearch
   └──── define_bounding_box(self)

        Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
   └──── nearest_k_search_for_point(self, PointCloud pc, int index, int k=1)

        Find the k nearest neighbours and squared distances for the point
        at pc[index]
   └──── VoxelSearch(self, PointCloud pc)

        Search for all neighbors of query point that are within a given voxel
   └──── add_points_from_input_cloud(self)

        Add points from input point cloud to octree
   └──── nearest_k_search_for_cloud(self, PointCloud pc, int k=1)

        Find the k nearest neighbours and squared distances for all points
        in the pointcloud
   └──── is_voxel_occupied_at_point(self, point)

        Check if voxel at given point coordinates exist
   └──── delete_voxel_at_point(self, point)

        Delete leaf node / voxel at given point
   └──── get_occupied_voxel_centers(self)

        Get list of centers of all occupied voxels
   └──── radius_search(self, point, double radius, unsigned int max_nn=0)

        Search for all neighbors of query point that are within a given radius
├──── pcl.OctreePointCloudChangeDetector_PointXYZI
   └──── define_bounding_box(self)

        Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
   └──── add_points_from_input_cloud(self)

        Add points from input point cloud to octree
   └──── is_voxel_occupied_at_point(self, point)

        Check if voxel at given point coordinates exist
   └──── get_PointIndicesFromNewVoxels(self)
   └──── delete_voxel_at_point(self, point)

        Delete leaf node / voxel at given point
   └──── get_occupied_voxel_centers(self)

        Get list of centers of all occupied voxels
├──── pcl.KdTreeFLANN_PointXYZRGBA
   └──── nearest_k_search_for_point(self, PointCloud_PointXYZRGBA pc, int index, int k=1)

        Find the k nearest neighbours and squared distances for the point
        at pc[index]
   └──── nearest_k_search_for_cloud(self, PointCloud_PointXYZRGBA pc, int k=1)

        Find the k nearest neighbours and squared distances for all points
        in the pointcloud
├──── pcl.PointCloud_PointNormal
   └──── from_list(self, _list)

        Fill this pointcloud from a list of 4-tuples
       
   └──── __reduce__(self)
   └──── to_list(self)

        Return this object as a list of 3-tuples
       
   └──── get_point(self, npy_intp row, npy_intp col)

        Return a point (3-tuple) at the given row/column
       
   └──── from_array(self, ndarray arr)

        Fill this object from a 2D numpy array (float32)
       
   └──── to_array(self)

        Return this object as a 2D numpy array (float32)
       
   └──── resize(self, npy_intp x)
├──── pcl.StatisticalOutlierRemovalFilter_PointXYZRGBA
   └──── set_mean_k(self, int k)

        Set the number of points (k) to use for mean distance estimation
   └──── set_negative(self, bool negative)

        Set whether the indices should be returned, or all points except the indices
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_InputCloud(self, PointCloud_PointXYZRGBA pc)
   └──── set_std_dev_mul_thresh(self, double std_mul)

        Set the standard deviation multiplier threshold
├──── pcl.ConditionAnd
   └──── add_Comparison2(self, field_name, CompareOp2 compOp, double thresh)
├──── pcl.ConcaveHull_PointXYZRGB
   └──── set_Alpha(self, double d)
   └──── reconstruct(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
├──── pcl.GeneralizedIterativeClosestPoint
   └──── gicp(self, PointCloud source, PointCloud target, max_iter=None)

        Align source to target using generalized iterative closest point (GICP)
├──── pcl.MovingLeastSquares
   └──── set_polynomial_fit(self, bool fit)

        Sets whether the surface and normal are approximated using a polynomial,
        or only via tangent estimation
   └──── set_polynomial_order(self, bool order)

        Set the order of the polynomial to be fit
   └──── set_search_radius(self, double radius)

        Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting
   └──── set_Search_Method(self, KdTree kdtree)
   └──── set_Compute_Normals(self, bool flag)
   └──── process(self)

        Apply the smoothing according to the previously set values and return
        a new PointCloud
       
├──── pcl.KdTree
├──── pcl.VFHEstimation
   └──── set_SearchMethod(self, KdTree kdtree)
   └──── set_KSearch(self, int param)
├──── pcl.IterativeClosestPointNonLinear
   └──── icp_nl(self, PointCloud source, PointCloud target, max_iter=None)

        Align source to target using generalized non-linear ICP (ICP-NL)
├──── pcl.ProjectInliers
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── get_copy_all_data(self)
   └──── get_model_type(self)
   └──── set_model_type(self, SacModel m)
   └──── set_copy_all_data(self, bool m)
├──── pcl.ApproximateVoxelGrid_PointXYZRGBA
   └──── set_leaf_size(self, float x, float y, float z)

        Set the voxel grid leaf size
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_InputCloud(self, PointCloud_PointXYZRGBA pc)
├──── pcl.ApproximateVoxelGrid_PointXYZRGB
   └──── set_leaf_size(self, float x, float y, float z)

        Set the voxel grid leaf size
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_InputCloud(self, PointCloud_PointXYZRGB pc)
├──── pcl.OctreePointCloud2Buf_PointXYZI
   └──── set_input_cloud(self, PointCloud_PointXYZI pc)

        Provide a pointer to the input data set
   └──── delete_tree(self)

        Delete the octree structure and its leaf nodes
├──── pcl.ApproximateVoxelGrid_PointXYZI
   └──── set_leaf_size(self, float x, float y, float z)

        Set the voxel grid leaf size
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_InputCloud(self, PointCloud_PointXYZI pc)
├──── pcl.OctreePointCloudChangeDetector_PointXYZRGB
   └──── define_bounding_box(self)

        Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
   └──── add_points_from_input_cloud(self)

        Add points from input point cloud to octree
   └──── is_voxel_occupied_at_point(self, point)

        Check if voxel at given point coordinates exist
   └──── get_PointIndicesFromNewVoxels(self)
   └──── delete_voxel_at_point(self, point)

        Delete leaf node / voxel at given point
   └──── get_occupied_voxel_centers(self)

        Get list of centers of all occupied voxels
├──── pcl.SampleConsensusModelPlane
├──── pcl.PointCloud_PointXYZRGBA
   └──── from_list(self, _list)

       Fill this pointcloud from a list of 4-tuples
       
   └──── __reduce__(self)
   └──── from_array(self, ndarray arr)

        Fill this object from a 2D numpy array (float32)
       
   └──── _to_ply_file(self, const char *f, bool binary=False)
   └──── to_array(self)

        Return this object as a 2D numpy array (float32)
       
   └──── extract(self, pyindices, bool negative=False)

        Given a list of indices of points in the pointcloud, return a
        new pointcloud containing only those points
   └──── make_voxel_grid_filter(self)

        Return a pcl
   └──── make_segmenter(self)

        Return a pcl
   └──── _from_obj_file(self, const char *s)
   └──── to_file(self, const char *fname, bool ascii=True)
Save pointcloud to a file in PCD format
   └──── make_passthrough_filter(self)

        Return a pcl
   └──── _from_ply_file(self, const char *s)
   └──── make_kdtree_flann(self)

        Return a pcl
   └──── _from_pcd_file(self, const char *s)
   └──── resize(self, npy_intp x)
   └──── make_statistical_outlier_filter(self)

         Return a pcl
   └──── from_file(self, char *f)

        Fill this pointcloud from a file (a local path)
   └──── _to_pcd_file(self, const char *f, bool binary=False)
   └──── to_list(self)

        Return this object as a list of 3-tuples
       
   └──── get_point(self, npy_intp row, npy_intp col)

        Return a point (3-tuple) at the given row/column
       
   └──── make_moving_least_squares(self)

        Return a pcl
   └──── make_segmenter_normals(self, int ksearch=-1, double searchRadius=-1
├──── pcl.SampleConsensusModelSphere
├──── pcl.KdTreeFLANN
   └──── radius_search_for_cloud(self, PointCloud pc, double radius, unsigned int max_nn=0)

        Find the radius and squared distances for all points
        in the pointcloud
   └──── nearest_k_search_for_point(self, PointCloud pc, int index, int k=1)

        Find the k nearest neighbours and squared distances for the point
        at pc[index]
   └──── nearest_k_search_for_cloud(self, PointCloud pc, int k=1)

        Find the k nearest neighbours and squared distances for all points
        in the pointcloud
├──── pcl.SampleConsensusModelCylinder
├──── pcl.MomentOfInertiaEstimation
   └──── get_AABB(self)
   └──── get_EigenVectors(self)
   └──── compute(self)
   └──── get_EigenValues(self)
   └──── get_Eccentricity(self)
   └──── get_OBB(self)
   └──── set_InputCloud(self, PointCloud pc)
   └──── get_MassCenter(self)
   └──── get_MomentOfInertia(self)
├──── pcl.NormalDistributionsTransform
   └──── set_OulierRatio(self, double outlier_ratio)
   └──── get_TransformationProbability(self)
   └──── set_StepSize(self, double step_size)
   └──── get_OulierRatio(self)
   └──── set_InputTarget(self)
   └──── set_Resolution(self, float resolution)
   └──── get_FinalNumIteration(self)
   └──── get_Resolution(self)
   └──── get_StepSize(self)
├──── pcl.SampleConsensusModel
├──── pcl.CropBox
   └──── set_Rotation(self, rx, ry, rz)
   └──── set_Min(self, minx, miny, minz, mins)
   └──── get_Negative(self)
   └──── set_MinMax(self, minx, miny, minz, mins, maxx, maxy, maxz, maxs)
   └──── set_Negative(self, bool flag)
   └──── set_Max(self, maxx, maxy, maxz, maxs)
   └──── get_RemovedIndices(self)
   └──── filter(self)
   └──── set_Translation(self, tx, ty, tz)
   └──── set_InputCloud(self, PointCloud pc)
├──── pcl.MovingLeastSquares_PointXYZRGBA
   └──── set_polynomial_fit(self, bool fit)

        Sets whether the surface and normal are approximated using a polynomial,
        or only via tangent estimation
   └──── set_polynomial_order(self, bool order)

        Set the order of the polynomial to be fit
   └──── set_search_radius(self, double radius)

        Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting
   └──── process(self)

        Apply the smoothing according to the previously set values and return
        a new pointcloud
       
├──── pcl.IntegralImageNormalEstimation
   └──── set_NormalEstimation_Method_SIMPLE_3D_GRADIENT(self)
   └──── set_NormalEstimation_Method_COVARIANCE_MATRIX(self)
   └──── set_NormalSmoothingSize(self, double param)
   └──── set_NormalEstimation_Method_AVERAGE_3D_GRADIENT(self)
   └──── set_MaxDepthChange_Factor(self, double param)
   └──── set_NormalEstimation_Method_AVERAGE_DEPTH_CHANGE(self)
   └──── compute(self)
├──── pcl.VoxelGridFilter_PointXYZRGBA
   └──── set_leaf_size(self, float x, float y, float z)

        Set the voxel grid leaf size
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
├──── pcl.Segmentation_PointXYZRGBA
   └──── set_method_type(self, int m)
   └──── set_distance_threshold(self, float d)
   └──── set_model_type(self, SacModel m)
   └──── segment(self)
   └──── set_optimize_coefficients(self, bool b)
├──── pcl.PassThroughFilter
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_filter_limits(self, float filter_min, float filter_max)
   └──── set_filter_field_name(self, field_name)
├──── pcl.OctreePointCloudChangeDetector
   └──── define_bounding_box(self)

        Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
   └──── add_points_from_input_cloud(self)

        Add points from input point cloud to octree
   └──── is_voxel_occupied_at_point(self, point)

        Check if voxel at given point coordinates exist
   └──── get_PointIndicesFromNewVoxels(self)
   └──── delete_voxel_at_point(self, point)

        Delete leaf node / voxel at given point
   └──── get_occupied_voxel_centers(self)

        Get list of centers of all occupied voxels
   └──── switchBuffers(self)
├──── pcl.HarrisKeypoint3D
   └──── compute(self)
   └──── set_NonMaxSupression(self, bool param)
   └──── set_RadiusSearch(self, double param)
   └──── set_Radius(self, float param)
├──── pcl.ConcaveHull_PointXYZI
   └──── set_Alpha(self, double d)
   └──── reconstruct(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
├──── pcl.CropHull
   └──── Filtering(self, PointCloud outputCloud)
   └──── filter(self)
   └──── set_InputCloud(self, PointCloud pc)
   └──── SetParameter(self, PointCloud points, Vertices vt)
├──── pcl.Segmentation_PointXYZRGB_Normal
   └──── set_method_type(self, int m)
   └──── get_eps_angle(self)
   └──── get_min_max_opening_angle(self)
   └──── set_distance_threshold(self, float d)
   └──── set_axis(self, double ax1, double ax2, double ax3)
   └──── set_radius_limits(self, float f1, float f2)
   └──── get_axis(self)
   └──── set_max_iterations(self, int i)
   └──── set_min_max_opening_angle(self, double min_angle, double max_angle)

        Set the minimum and maximum cone opening angles in radians for a cone model
   └──── set_eps_angle(self, double ea)
   └──── set_normal_distance_weight(self, float f)
   └──── set_model_type(self, SacModel m)
   └──── segment(self)
   └──── set_optimize_coefficients(self, bool b)
├──── pcl.PassThroughFilter_PointXYZI
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new PointCloud_PointXYZI
       
   └──── set_filter_limits(self, float filter_min, float filter_max)
   └──── set_filter_field_name(self, field_name)
├──── pcl.ApproximateVoxelGrid
   └──── set_leaf_size(self, float x, float y, float z)

        Set the voxel grid leaf size
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new pointcloud
       
   └──── set_InputCloud(self, PointCloud pc)
├──── pcl.OctreePointCloudSearch_PointXYZRGB
   └──── define_bounding_box(self)

        Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
   └──── add_points_from_input_cloud(self)

        Add points from input point cloud to octree
   └──── is_voxel_occupied_at_point(self, point)

        Check if voxel at given point coordinates exist
   └──── delete_voxel_at_point(self, point)

        Delete leaf node / voxel at given point
   └──── get_occupied_voxel_centers(self)

        Get list of centers of all occupied voxels
   └──── radius_search(self, point, double radius, unsigned int max_nn=0)

        Search for all neighbors of query point that are within a given radius
├──── pcl.PointCloud
   └──── make_octreeChangeDetector(self, double resolution)

        Return a pcl
   └──── from_list(self, _list)

        Fill this pointcloud from a list of 3-tuples
       
   └──── make_kdtree(self)

        Return a pcl
   └──── __reduce__(self)
   └──── make_ConcaveHull(self)

        Return a pcl
   └──── from_array(self, ndarray arr)

        Fill this object from a 2D numpy array (float32)
       
   └──── _to_ply_file(self, const char *f, bool binary=False)
   └──── make_RangeImage(self)
   └──── to_array(self)

        Return this object as a 2D numpy array (float32)
       
   └──── make_ConditionalRemoval(self, ConditionAnd range_conf)

        Return a pcl
   └──── extract(self, pyindices, bool negative=False)

        Given a list of indices of points in the pointcloud, return a
        new pointcloud containing only those points
   └──── make_GeneralizedIterativeClosestPoint(self)
   └──── make_octreeSearch(self, double resolution)

        Return a pcl
   └──── make_octree(self, double resolution)

        Return a pcl
   └──── make_ProjectInliers(self)

        Return a pclfil
   └──── make_NormalEstimation(self)
   └──── make_RegionGrowing(self, int ksearch=-1, double searchRadius=-1
   └──── make_IterativeClosestPointNonLinear(self)
   └──── make_voxel_grid_filter(self)

        Return a pcl
   └──── make_segmenter(self)

        Return a pcl
   └──── _from_obj_file(self, const char *s)
   └──── to_file(self, const char *fname, bool ascii=True)
Save pointcloud to a file in PCD format
   └──── make_ApproximateVoxelGrid(self)

        Return a pcl
   └──── make_VFHEstimation(self)
   └──── make_EuclideanClusterExtraction(self)
   └──── make_ConditionAnd(self)

        Return a pcl
   └──── make_passthrough_filter(self)

        Return a pcl
   └──── _from_ply_file(self, const char *s)
   └──── make_kdtree_flann(self)

        Return a pcl
   └──── _from_pcd_file(self, const char *s)
   └──── make_RadiusOutlierRemoval(self)

        Return a pclfil
   └──── resize(self, npy_intp x)
   └──── make_statistical_outlier_filter(self)

        Return a pcl
   └──── from_file(self, char *f)

        Fill this pointcloud from a file (a local path)
   └──── _to_pcd_file(self, const char *f, bool binary=False)
   └──── make_cropbox(self)

        Return a pcl
   └──── make_MomentOfInertiaEstimation(self)
   └──── make_IterativeClosestPoint(self)
   └──── to_list(self)

        Return this object as a list of 3-tuples
       
   └──── get_point(self, npy_intp row, npy_intp col)

        Return a point (3-tuple) at the given row/column
       
   └──── make_HarrisKeypoint3D(self)

        Return a pcl
   └──── make_moving_least_squares(self)

        Return a pcl
   └──── make_IntegralImageNormalEstimation(self)

        Return a pcl
   └──── make_segmenter_normals(self, int ksearch=-1, double searchRadius=-1
   └──── make_crophull(self)

        Return a pcl
├──── pcl.RangeImages
   └──── CreateFromPointCloud(self, PointCloud cloud, float angular_resolution, float max_angle_width, float max_angle_height, CoordinateFrame2 coordinate_frame, float noise_level, float min_range, int border_size)
   └──── SetUnseenToMaxRange(self)
   └──── SetAngularResolution(self, float angular_resolution_x, float angular_resolution_y)
   └──── IntegrateFarRanges(self, PointCloud_PointWithViewpoint viewpoint)
├──── pcl.IterativeClosestPoint
   └──── icp(self, PointCloud source, PointCloud target, max_iter=None)

        Align source to target using iterative closest point (ICP)
   └──── set_InputTarget(self, PointCloud cloud)
├──── pcl.Segmentation_PointXYZI_Normal
   └──── set_method_type(self, int m)
   └──── get_eps_angle(self)
   └──── get_min_max_opening_angle(self)
   └──── set_distance_threshold(self, float d)
   └──── set_axis(self, double ax1, double ax2, double ax3)
   └──── set_radius_limits(self, float f1, float f2)
   └──── get_axis(self)
   └──── set_max_iterations(self, int i)
   └──── set_min_max_opening_angle(self, double min_angle, double max_angle)

        Set the minimum and maximum cone opening angles in radians for a cone model
   └──── set_eps_angle(self, double ea)
   └──── set_normal_distance_weight(self, float f)
   └──── set_model_type(self, SacModel m)
   └──── segment(self)
   └──── set_optimize_coefficients(self, bool b)
├──── pcl.PassThroughFilter_PointXYZRGB
   └──── filter(self)

        Apply the filter according to the previously set parameters and return
        a new PointCloud_PointXYZRGB
       
   └──── set_filter_limits(self, float filter_min, float filter_max)
   └──── set_filter_field_name(self, field_name)

('SACMODEL_LINE', 1)
('SACMODEL_PARALLEL_PLANE', 15)
('SACMODEL_CYLINDER', 5)
('SAC_PROSAC', 6)
('SAC_LMEDS', 1)
('SACMODEL_PARALLEL_LINES', 10)
('SAC_MSAC', 2)
('SACMODEL_REGISTRATION', 13)
('SAC_RRANSAC', 3)
('SACMODEL_NORMAL_PARALLEL_PLANE', 16)
('SACMODEL_NORMAL_SPHERE', 12)
('SACMODEL_CIRCLE2D', 2)
('SACMODEL_TORUS', 7)
('SACMODEL_STICK', 17)
('SACMODEL_CIRCLE3D', 3)
('SAC_RMSAC', 4)
('SACMODEL_NORMAL_PLANE', 11)
('SACMODEL_PERPENDICULAR_PLANE', 9)
('SAC_RANSAC', 0)
('SACMODEL_CONE', 6)
('SAC_MLESAC', 5)
('SACMODEL_SPHERE', 4)
('SACMODEL_PARALLEL_LINE', 8)
('SACMODEL_PLANE', 0)

('PCLVISUALIZER_OPACITY', 1)
('PCLVISUALIZER_REPRESENTATION', 5)
('PCLVISUALIZER_FONT_SIZE', 3)
('PCLVISUALIZER_POINT_SIZE', 0)
('PCLVISUALIZER_REPRESENTATION_WIREFRAME', 1)
('PCLVISUALIZER_IMMEDIATE_RENDERING', 6)
('PCLVISUALIZER_REPRESENTATION_POINTS', 0)
('PCLVISUALIZER_REPRESENTATION_SURFACE', 2)
('PCLVISUALIZER_LINE_WIDTH', 2)
('PCLVISUALIZER_COLOR', 4)