Detailed Description

Overview

The pcl_filters library contains outlier and noise removal mechanisms for 3D point cloud data filtering applications.

An example of noise removal is presented in the figure below. Due to measurement errors, certain datasets present a large number of shadow points. This complicates the estimation of local point cloud 3D features. Some of these outliers can be filtered by performing a statistical analysis on each point's neighborhood, and trimming those which do not meet a certain criteria. The sparse outlier removal implementation in PCL is based on the computation of the distribution of point to neighbors distances in the input dataset. For each point, the mean distance from it to all its neighbors is computed. By assuming that the resulted distribution is Gaussian with a mean and a standard deviation, all points whose mean distances are outside an interval defined by the global distances mean and standard deviation can be considered as outliers and trimmed from the dataset.

Requirements

Classes
class	pcl::ApproximateVoxelGrid< PointT >
	ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...

class	pcl::BilateralFilter< PointT >
	A bilateral filter implementation for point cloud data. More...

class	pcl::BoxClipper3D< PointT >
	Implementation of a box clipper in 3D. Actually it allows affine transformations, thus any parallelepiped in general pose. The affine transformation is used to transform the point before clipping it using a cube centered at origin and with an extend of -1 to +1 in each dimension. More...

class	pcl::Clipper3D< PointT >
	Base class for 3D clipper objects. More...

class	pcl::ConditionalRemoval< PointT >
	ConditionalRemoval filters data that satisfies certain conditions. More...

class	pcl::filters::Convolution< PointIn, PointOut >
	Convolution is a mathematical operation on two functions f and g, producing a third function that is typically viewed as a modified version of one of the original functions. More...

class	pcl::filters::ConvolvingKernel< PointInT, PointOutT >
	Class ConvolvingKernel base class for all convolving kernels. More...

class	pcl::filters::GaussianKernel< PointInT, PointOutT >
	Gaussian kernel implementation interface Use this as implementation reference. More...

class	pcl::filters::GaussianKernelRGB< PointInT, PointOutT >
	Gaussian kernel implementation interface with RGB channel handling Use this as implementation reference. More...

class	pcl::CovarianceSampling< PointT, PointNT >
	Point Cloud sampling based on the 6D covariances. More...

class	pcl::CropBox< PointT >
	CropBox is a filter that allows the user to filter all the data inside of a given box. More...

class	pcl::CropBox< pcl::PCLPointCloud2 >
	CropBox is a filter that allows the user to filter all the data inside of a given box. More...

class	pcl::CropHull< PointT >
	Filter points that lie inside or outside a 3D closed surface or 2D closed polygon, as generated by the ConvexHull or ConcaveHull classes. More...

class	pcl::experimental::advanced::FunctorFilter< PointT, FunctionObject >
	Filter point clouds and indices based on a function object passed in the ctor. More...

class	pcl::ExtractIndices< PointT >
	ExtractIndices extracts a set of indices from a point cloud. More...

class	pcl::ExtractIndices< pcl::PCLPointCloud2 >
	ExtractIndices extracts a set of indices from a point cloud. More...

class	pcl::FarthestPointSampling< PointT >
	FarthestPointSampling applies farthest point sampling using euclidean distance, starting with a random point, utilizing a naive method. More...

class	pcl::Filter< PointT >
	Filter represents the base filter class. More...

class	pcl::Filter< pcl::PCLPointCloud2 >
	Filter represents the base filter class. More...

class	pcl::FilterIndices< PointT >
	FilterIndices represents the base class for filters that are about binary point removal. More...

class	pcl::FilterIndices< pcl::PCLPointCloud2 >
	FilterIndices represents the base class for filters that are about binary point removal. More...

class	pcl::FrustumCulling< PointT >
	FrustumCulling filters points inside a frustum given by pose and field of view of the camera. More...

class	pcl::GridMinimum< PointT >
	GridMinimum assembles a local 2D grid over a given PointCloud, and downsamples the data. More...

class	pcl::LocalMaximum< PointT >
	LocalMaximum downsamples the cloud, by eliminating points that are locally maximal. More...

class	pcl::MedianFilter< PointT >
	Implementation of the median filter. More...

class	pcl::ModelOutlierRemoval< PointT >
	ModelOutlierRemoval filters points in a cloud based on the distance between model and point. More...

class	pcl::NormalRefinement< NormalT >
	Normal vector refinement class More...

class	pcl::NormalSpaceSampling< PointT, NormalT >
	NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every point. More...

class	pcl::PassThrough< PointT >
	PassThrough passes points in a cloud based on constraints for one particular field of the point type. More...

class	pcl::PassThrough< pcl::PCLPointCloud2 >
	PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints. More...

class	pcl::PlaneClipper3D< PointT >
	Implementation of a plane clipper in 3D. More...

class	pcl::ProjectInliers< PointT >
	ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud. More...

class	pcl::ProjectInliers< pcl::PCLPointCloud2 >
	ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud. More...

class	pcl::RadiusOutlierRemoval< PointT >
	RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have. More...

class	pcl::RadiusOutlierRemoval< pcl::PCLPointCloud2 >
	RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K. More...

class	pcl::RandomSample< PointT >
	RandomSample applies a random sampling with uniform probability. More...

class	pcl::RandomSample< pcl::PCLPointCloud2 >
	RandomSample applies a random sampling with uniform probability. More...

class	pcl::SamplingSurfaceNormal< PointT >
	SamplingSurfaceNormal divides the input space into grids until each grid contains a maximum of N points, and samples points randomly within each grid. More...

class	pcl::ShadowPoints< PointT, NormalT >
	ShadowPoints removes the ghost points appearing on edge discontinuties More...

class	pcl::StatisticalOutlierRemoval< PointT >
	StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. More...

class	pcl::StatisticalOutlierRemoval< pcl::PCLPointCloud2 >
	StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. More...

class	pcl::UniformSampling< PointT >
	UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...

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

class	pcl::VoxelGrid< pcl::PCLPointCloud2 >
	VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data. More...

class	pcl::VoxelGridOcclusionEstimation< PointT >
	VoxelGrid to estimate occluded space in the scene. More...

Functions
template<typename PointT >
void	pcl::removeNaNFromPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, Indices &index)
	Removes points with x, y, or z equal to NaN. More...

template<typename PointT >
void	pcl::removeNaNNormalsFromPointCloud (const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, Indices &index)
	Removes points that have their normals invalid (i.e., equal to NaN) More...

template<typename PointT >
void	pcl::removeNaNFromPointCloud (const pcl::PointCloud< PointT > &cloud_in, Indices &index)
	Removes points with x, y, or z equal to NaN (dry run). More...

template<typename PointT >
PCL_EXPORTS void	pcl::applyMorphologicalOperator (const typename pcl::PointCloud< PointT >::ConstPtr &cloud_in, float resolution, const int morphological_operator, pcl::PointCloud< PointT > &cloud_out)
	Apply morphological operator to the z dimension of the input point cloud. More...

template<typename NormalT >
std::vector< float >	pcl::assignNormalWeights (const PointCloud< NormalT > &cloud, index_t index, const Indices &k_indices, const std::vector< float > &k_sqr_distances)
	Assign weights of nearby normals used for refinement. More...

template<typename NormalT >
bool	pcl::refineNormal (const PointCloud< NormalT > &cloud, int index, const Indices &k_indices, const std::vector< float > &k_sqr_distances, NormalT &point)
	Refine an indexed point based on its neighbors, this function only writes to the normal_* fields. More...

Eigen::MatrixXi	pcl::getHalfNeighborCellIndices ()
	Get the relative cell indices of the "upper half" 13 neighbors. More...

Eigen::MatrixXi	pcl::getAllNeighborCellIndices ()
	Get the relative cell indices of all the 26 neighbors. More...

template<typename PointT >
void	pcl::getMinMax3D (const typename pcl::PointCloud< PointT >::ConstPtr &cloud, const std::string &distance_field_name, float min_distance, float max_distance, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, bool limit_negative=false)
	Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud, without considering points outside of a distance threshold from the laser origin. More...

template<typename PointT >
void	pcl::getMinMax3D (const typename pcl::PointCloud< PointT >::ConstPtr &cloud, const Indices &indices, const std::string &distance_field_name, float min_distance, float max_distance, Eigen::Vector4f &min_pt, Eigen::Vector4f &max_pt, bool limit_negative=false)
	Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud, without considering points outside of a distance threshold from the laser origin. More...

Function Documentation

◆ applyMorphologicalOperator()

template<typename PointT >

PCL_EXPORTS void pcl::applyMorphologicalOperator	(	const typename pcl::PointCloud< PointT >::ConstPtr &	cloud_in,
		float	resolution,
		const int	morphological_operator,
		pcl::PointCloud< PointT > &	cloud_out
	)

#include <pcl/filters/morphological_filter.h>

Apply morphological operator to the z dimension of the input point cloud.

Parameters

[in]	cloud_in	the input point cloud dataset
[in]	resolution	the window size to be used for the morphological operation
[in]	morphological_operator	the morphological operator to apply (open, close, dilate, erode)
[out]	cloud_out	the resultant output point cloud dataset

Definition at line 57 of file morphological_filter.hpp.

References pcl::octree::OctreePointCloud< PointT, LeafContainerT, BranchContainerT, OctreeT >::addPointsFromInputCloud(), pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::boxSearch(), pcl::copyPointCloud(), pcl::PointCloud< PointT >::empty(), pcl::MORPH_CLOSE, pcl::MORPH_DILATE, pcl::MORPH_ERODE, pcl::MORPH_OPEN, pcl::octree::OctreePointCloud< PointT, LeafContainerT, BranchContainerT, OctreeT >::setInputCloud(), pcl::PointCloud< PointT >::size(), and pcl::PointCloud< PointT >::swap().

◆ assignNormalWeights()

template<typename NormalT >

std::vector<float> pcl::assignNormalWeights	(	const PointCloud< NormalT > &	cloud,
		index_t	index,
		const Indices &	k_indices,
		const std::vector< float > &	k_sqr_distances
	)

inline

#include <pcl/filters/normal_refinement.h>

Assign weights of nearby normals used for refinement.

Todo:: Currently, this function equalizes all weights to 1

Parameters

cloud	the point cloud data
index	a valid index in cloud representing a valid (i.e., finite) query point
k_indices	indices of neighboring points
k_sqr_distances	squared distances to the neighboring points

Returns: weights

Definition at line 56 of file normal_refinement.h.

References pcl::utils::ignore().

Referenced by pcl::refineNormal().

◆ getAllNeighborCellIndices()

Eigen::MatrixXi pcl::getAllNeighborCellIndices ( )

inline

#include <pcl/filters/voxel_grid.h>

Get the relative cell indices of all the 26 neighbors.

Note: Useful in combination with getNeighborCentroidIndices() from VoxelGrid

Definition at line 153 of file voxel_grid.h.

References pcl::getHalfNeighborCellIndices().

Referenced by pcl::VoxelGridCovariance< PointT >::getAllNeighborsAtPoint(), and pcl::VoxelGridCovariance< PointTarget >::getFaceNeighborsAtPoint().

◆ getHalfNeighborCellIndices()

Eigen::MatrixXi pcl::getHalfNeighborCellIndices ( )

inline

#include <pcl/filters/voxel_grid.h>

Get the relative cell indices of the "upper half" 13 neighbors.

Note: Useful in combination with getNeighborCentroidIndices() from VoxelGrid

Definition at line 116 of file voxel_grid.h.

Referenced by pcl::getAllNeighborCellIndices().

◆ getMinMax3D() [1/2]

template<typename PointT >

void pcl::getMinMax3D	(	const typename pcl::PointCloud< PointT >::ConstPtr &	cloud,
		const Indices &	indices,
		const std::string &	distance_field_name,
		float	min_distance,
		float	max_distance,
		Eigen::Vector4f &	min_pt,
		Eigen::Vector4f &	max_pt,
		bool	limit_negative = `false`
	)

#include <pcl/filters/voxel_grid.h>

Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud, without considering points outside of a distance threshold from the laser origin.

Parameters

[in]	cloud	the point cloud data message
[in]	indices	the vector of indices to use
[in]	distance_field_name	the field name that contains the distance values
[in]	min_distance	the minimum distance a point will be considered from
[in]	max_distance	the maximum distance a point will be considered to
[out]	min_pt	the resultant minimum bounds
[out]	max_pt	the resultant maximum bounds
[in]	limit_negative	if set to true, then all points outside of the interval (min_distance;max_distace) are considered

Definition at line 132 of file voxel_grid.hpp.

References pcl::PointCloud< PointT >::is_dense.

◆ getMinMax3D() [2/2]

template<typename PointT >

void pcl::getMinMax3D	(	const typename pcl::PointCloud< PointT >::ConstPtr &	cloud,
		const std::string &	distance_field_name,
		float	min_distance,
		float	max_distance,
		Eigen::Vector4f &	min_pt,
		Eigen::Vector4f &	max_pt,
		bool	limit_negative = `false`
	)

#include <pcl/filters/voxel_grid.h>

Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud, without considering points outside of a distance threshold from the laser origin.

Parameters

[in]	cloud	the point cloud data message
[in]	distance_field_name	the field name that contains the distance values
[in]	min_distance	the minimum distance a point will be considered from
[in]	max_distance	the maximum distance a point will be considered to
[out]	min_pt	the resultant minimum bounds
[out]	max_pt	the resultant maximum bounds
[in]	limit_negative	if set to true, then all points outside of the interval (min_distance;max_distace) are considered

Definition at line 51 of file voxel_grid.hpp.

References pcl::PointCloud< PointT >::is_dense, and pcl::isXYZFinite().

◆ refineNormal()

template<typename NormalT >

bool pcl::refineNormal	(	const PointCloud< NormalT > &	cloud,
		int	index,
		const Indices &	k_indices,
		const std::vector< float > &	k_sqr_distances,
		NormalT &	point
	)

inline

#include <pcl/filters/normal_refinement.h>

Refine an indexed point based on its neighbors, this function only writes to the normal_* fields.

Note: If the indexed point has only NaNs in its neighborhood, the resulting normal will be zero.

Parameters

cloud	the point cloud data
index	a valid index in cloud representing a valid (i.e., finite) query point
k_indices	indices of neighboring points
k_sqr_distances	squared distances to the neighboring points
point	the output point, only normal_* fields are written

Returns: false if an error occurred (norm of summed normals zero or all neighbors NaN)

Definition at line 87 of file normal_refinement.h.

References pcl::assignNormalWeights().

Referenced by pcl::NormalRefinement< NormalT >::applyFilter().

◆ removeNaNFromPointCloud() [1/2]

template<typename PointT >

void pcl::removeNaNFromPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		Indices &	index
	)

#include <pcl/filters/filter_indices.h>

Removes points with x, y, or z equal to NaN (dry run).

This function only computes the mapping between the points in the input cloud and the cloud that would result from filtering. It does not actually construct and output the filtered cloud.

Note: This function does not modify the input point cloud!

Parameters

cloud_in	the input point cloud
index	the mapping (ordered): filtered_cloud[i] = cloud_in[index[i]]

See also: removeNaNFromPointCloud

Definition at line 44 of file filter_indices.hpp.

References pcl::PointCloud< PointT >::is_dense, and pcl::PointCloud< PointT >::size().

◆ removeNaNFromPointCloud() [2/2]

template<typename PointT >

void pcl::removeNaNFromPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		Indices &	index
	)

#include <pcl/filters/filter.h>

Removes points with x, y, or z equal to NaN.

Parameters

[in]	cloud_in	the input point cloud
[out]	cloud_out	the output point cloud
[out]	index	the mapping (ordered): cloud_out[i] = cloud_in[index[i]]

Note: The density of the point cloud is lost.; Can be called with cloud_in == cloud_out

Definition at line 46 of file filter.hpp.

References pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::PointCloud< PointT >::is_dense, pcl::PointCloud< PointT >::resize(), pcl::PointCloud< PointT >::sensor_orientation_, pcl::PointCloud< PointT >::sensor_origin_, pcl::PointCloud< PointT >::size(), and pcl::PointCloud< PointT >::width.

Referenced by pcl::kinfuLS::WorldModel< PointT >::cleanWorldFromNans(), and pcl::kinfuLS::WorldModel< PointT >::getWorldAsCubes().

◆ removeNaNNormalsFromPointCloud()

template<typename PointT >

void pcl::removeNaNNormalsFromPointCloud	(	const pcl::PointCloud< PointT > &	cloud_in,
		pcl::PointCloud< PointT > &	cloud_out,
		Indices &	index
	)

#include <pcl/filters/filter.h>

Removes points that have their normals invalid (i.e., equal to NaN)

Parameters

[in]	cloud_in	the input point cloud
[out]	cloud_out	the output point cloud
[out]	index	the mapping (ordered): cloud_out[i] = cloud_in[index[i]]

Note: The density of the point cloud is lost.; Can be called with cloud_in == cloud_out

Definition at line 99 of file filter.hpp.

References pcl::PointCloud< PointT >::header, pcl::PointCloud< PointT >::height, pcl::PointCloud< PointT >::is_dense, pcl::isFinite(), pcl::PointCloud< PointT >::resize(), pcl::PointCloud< PointT >::sensor_orientation_, pcl::PointCloud< PointT >::sensor_origin_, pcl::PointCloud< PointT >::size(), and pcl::PointCloud< PointT >::width.

Detailed Description

Overview

Requirements

Classes

Functions

Function Documentation

◆ applyMorphologicalOperator()

◆ assignNormalWeights()

◆ getAllNeighborCellIndices()

◆ getHalfNeighborCellIndices()

◆ getMinMax3D() [1/2]

◆ getMinMax3D() [2/2]

◆ refineNormal()

◆ removeNaNFromPointCloud() [1/2]

◆ removeNaNFromPointCloud() [2/2]

◆ removeNaNNormalsFromPointCloud()