Detailed Description

Overview

The pcl_features library contains data structures and mechanisms for 3D feature estimation from point cloud data. 3D features are representations at a certain 3D point or position in space, which describe geometrical patterns based on the information available around the point. The data space selected around the query point is usually referred as the k-neighborhood.

The following figure shows a simple example of a selected query point, and its selected k-neighborhood.

An example of two of the most widely used geometric point features are the underlying surface's estimated curvature and normal at a query point p. Both of them are considered local features, as they characterize a point using the information provided by its k closest point neighbors. For determining these neighbors efficiently, the input dataset is usually split into smaller chunks using spatial decomposition techniques such as octrees or kD-trees (see the figure below - left: kD-tree, right: octree), and then closest point searches are performed in that space. Depending on the application one can opt for either determining a fixed number of k points in the vicinity of p, or all points which are found inside of a sphere of radius r centered at p. Unarguably, one the easiest methods for estimating the surface normals and curvature changes at a point p is to perform an eigendecomposition (i.e. compute the eigenvectors and eigenvalues) of the k-neighborhood point surface patch. Thus, the eigenvector corresponding to the smallest eigenvalue will approximate the surface normal n at point p, while the surface curvature change will be estimated from the eigenvalues as:

$\frac{\lambda_0}{\lambda_0 + \lambda_1 + \lambda_2}$ , where $\lambda_0 < \lambda_1 < \lambda_2$ .

Please visit http://www.pointclouds.org for more information.

Requirements

Classes
class	pcl::ShapeContext3DEstimation< PointInT, PointNT, PointOutT >
	ShapeContext3DEstimation implements the 3D shape context descriptor as described in: More...

class	pcl::BOARDLocalReferenceFrameEstimation< PointInT, PointNT, PointOutT >
	BOARDLocalReferenceFrameEstimation implements the BOrder Aware Repeatable Directions algorithm for local reference frame estimation as described here: More...

class	pcl::BoundaryEstimation< PointInT, PointNT, PointOutT >
	BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion. More...

class	pcl::BRISK2DEstimation< PointInT, PointOutT, KeypointT, IntensityT >
	Implementation of the BRISK-descriptor, based on the original code and paper reference by. More...

class	pcl::CRHEstimation< PointInT, PointNT, PointOutT >
	CRHEstimation estimates the Camera Roll Histogram (CRH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in: More...

class	pcl::CVFHEstimation< PointInT, PointNT, PointOutT >
	CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in: More...

class	pcl::DifferenceOfNormalsEstimation< PointInT, PointNT, PointOutT >
	A Difference of Normals (DoN) scale filter implementation for point cloud data. More...

class	pcl::ESFEstimation< PointInT, PointOutT >
	ESFEstimation estimates the ensemble of shape functions descriptors for a given point cloud dataset containing points. More...

class	pcl::Feature< PointInT, PointOutT >
	Feature represents the base feature class. More...

class	pcl::FeatureWithLocalReferenceFrames< PointInT, PointRFT >
	FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which need a local reference frame at each input keypoint. More...

class	pcl::FLARELocalReferenceFrameEstimation< PointInT, PointNT, PointOutT, SignedDistanceT >
	FLARELocalReferenceFrameEstimation implements the Fast LocAl Reference framE algorithm for local reference frame estimation as described here: More...

class	pcl::FPFHEstimation< PointInT, PointNT, PointOutT >
	FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals. More...

class	pcl::FPFHEstimationOMP< PointInT, PointNT, PointOutT >
	FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard. More...

class	pcl::GASDEstimation< PointInT, PointOutT >
	GASDEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given point cloud dataset given XYZ data. More...

class	pcl::GASDColorEstimation< PointInT, PointOutT >
	GASDColorEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given point cloud dataset given XYZ and RGB data. More...

class	pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >
	GFPFHEstimation estimates the Global Fast Point Feature Histogram (GFPFH) descriptor for a given point cloud dataset containing points and labels. More...

class	pcl::GRSDEstimation< PointInT, PointNT, PointOutT >
	GRSDEstimation estimates the Global Radius-based Surface Descriptor (GRSD) for a given point cloud dataset containing points and normals. More...

class	pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT >
	IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values. More...

class	pcl::IntensitySpinEstimation< PointInT, PointOutT >
	IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity. More...

class	pcl::MomentInvariantsEstimation< PointInT, PointOutT >
	MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point. More...

class	pcl::Narf
	NARF (Normal Aligned Radial Features) is a point feature descriptor type for 3D data. More...

class	pcl::NarfDescriptor
	Computes NARF feature descriptors for points in a range image See B. More...

class	pcl::NormalEstimation< PointInT, PointOutT >
	NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point. More...

class	pcl::NormalEstimationOMP< PointInT, PointOutT >
	NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard. More...

class	pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >
	OURCVFHEstimation estimates the Oriented, Unique and Repetable Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset given XYZ data and normals, as presented in: More...

class	pcl::PFHEstimation< PointInT, PointNT, PointOutT >
	PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals. More...

class	pcl::PFHRGBEstimation< PointInT, PointNT, PointOutT >
	Similar to the Point Feature Histogram descriptor, but also takes color into account. More...

class	pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT >
	PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals. More...

class	pcl::RangeImageBorderExtractor
	Extract obstacle borders from range images, meaning positions where there is a transition from foreground to background. More...

class	pcl::RIFTEstimation< PointInT, GradientT, PointOutT >
	RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity. More...

class	pcl::RSDEstimation< PointInT, PointNT, PointOutT >
	RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves) for a given point cloud dataset containing points and normals. More...

class	pcl::SHOTEstimationBase< PointInT, PointNT, PointOutT, PointRFT >
	SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals. More...

class	pcl::SHOTEstimation< PointInT, PointNT, PointOutT, PointRFT >
	SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals. More...

class	pcl::SHOTColorEstimation< PointInT, PointNT, PointOutT, PointRFT >
	SHOTColorEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors. More...

class	pcl::SHOTLocalReferenceFrameEstimation< PointInT, PointOutT >
	SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor. More...

class	pcl::SHOTLocalReferenceFrameEstimationOMP< PointInT, PointOutT >
	SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor. More...

class	pcl::SHOTEstimationOMP< PointInT, PointNT, PointOutT, PointRFT >
	SHOTEstimationOMP estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard. More...

class	pcl::SHOTColorEstimationOMP< PointInT, PointNT, PointOutT, PointRFT >
	SHOTColorEstimationOMP estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors, in parallel, using the OpenMP standard. More...

class	pcl::SpinImageEstimation< PointInT, PointNT, PointOutT >
	Estimates spin-image descriptors in the given input points. More...

class	pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT >
	UniqueShapeContext implements the Unique Shape Context Descriptor described here: More...

class	pcl::VFHEstimation< PointInT, PointNT, PointOutT >
	VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals. More...

Functions
void	pcl::solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix, const Eigen::Vector4f &point, Eigen::Vector4f &plane_parameters, float &curvature)
	Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature. More...

void	pcl::solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix, float &nx, float &ny, float &nz, float &curvature)
	Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature. More...

template<typename PointT >
bool	pcl::computePointNormal (const pcl::PointCloud< PointT > &cloud, Eigen::Vector4f &plane_parameters, float &curvature)
	Compute the Least-Squares plane fit for a given set of points, and return the estimated plane parameters together with the surface curvature. More...

template<typename PointT >
bool	pcl::computePointNormal (const pcl::PointCloud< PointT > &cloud, const pcl::Indices &indices, Eigen::Vector4f &plane_parameters, float &curvature)
	Compute the Least-Squares plane fit for a given set of points, using their indices, and return the estimated plane parameters together with the surface curvature. More...

template<typename PointT , typename Scalar >
void	pcl::flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z, Eigen::Matrix< Scalar, 4, 1 > &normal)
	Flip (in place) the estimated normal of a point towards a given viewpoint. More...

template<typename PointT , typename Scalar >
void	pcl::flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z, Eigen::Matrix< Scalar, 3, 1 > &normal)
	Flip (in place) the estimated normal of a point towards a given viewpoint. More...

template<typename PointT >
void	pcl::flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z, float &nx, float &ny, float &nz)
	Flip (in place) the estimated normal of a point towards a given viewpoint. More...

template<typename PointNT >
bool	pcl::flipNormalTowardsNormalsMean (pcl::PointCloud< PointNT > const &normal_cloud, pcl::Indices const &normal_indices, Eigen::Vector3f &normal)
	Flip (in place) normal to get the same sign of the mean of the normals specified by normal_indices. More...

PCL_EXPORTS bool	pcl::computePairFeatures (const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, float &f1, float &f2, float &f3, float &f4)
	Compute the 4-tuple representation containing the three angles and one distance between two points represented by Cartesian coordinates and normals. More...

template<int N>
void	pcl::getFeaturePointCloud (const std::vector< Eigen::MatrixXf, Eigen::aligned_allocator< Eigen::MatrixXf > > &histograms2D, PointCloud< Histogram< N > > &histogramsPC)
	Transform a list of 2D matrices into a point cloud containing the values in a vector (Histogram<N>). More...

template<typename PointInT , typename PointNT , typename PointOutT >
Eigen::MatrixXf	pcl::computeRSD (const pcl::PointCloud< PointInT > &surface, const pcl::PointCloud< PointNT > &normals, const pcl::Indices &indices, double max_dist, int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram=false)
	Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals. More...

template<typename PointNT , typename PointOutT >
Eigen::MatrixXf	pcl::computeRSD (const pcl::PointCloud< PointNT > &normals, const pcl::Indices &indices, const std::vector< float > &sqr_dists, double max_dist, int nr_subdiv, double plane_radius, PointOutT &radii, bool compute_histogram=false)
	Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals. More...

Function Documentation

◆ computePairFeatures()

PCL_EXPORTS bool pcl::computePairFeatures	(	const Eigen::Vector4f &	p1,
		const Eigen::Vector4f &	n1,
		const Eigen::Vector4f &	p2,
		const Eigen::Vector4f &	n2,
		float &	f1,
		float &	f2,
		float &	f3,
		float &	f4
	)

#include <pcl/features/pfh_tools.h>

Compute the 4-tuple representation containing the three angles and one distance between two points represented by Cartesian coordinates and normals.

Note: For explanations about the features, please see the literature mentioned above (the order of the features might be different).

Parameters

[in]	p1	the first XYZ point
[in]	n1	the first surface normal
[in]	p2	the second XYZ point
[in]	n2	the second surface normal
[out]	f1	the first angular feature (angle between the projection of nq_idx and u)
[out]	f2	the second angular feature (angle between nq_idx and v)
[out]	f3	the third angular feature (angle between np_idx and \|p_idx - q_idx\|)
[out]	f4	the distance feature (p_idx - q_idx)

Note: For efficiency reasons, we assume that the point data passed to the method is finite.

Referenced by pcl::FPFHEstimation< PointInT, PointNT, PointOutT >::computePairFeatures(), pcl::PFHEstimation< PointInT, PointNT, PointOutT >::computePairFeatures(), pcl::PFHEstimation< PointInT, PointNT, PointOutT >::computePointPFHSignature(), pcl::VFHEstimation< PointInT, PointNT, PointOutT >::computePointSPFHSignature(), and pcl::FPFHEstimation< PointInT, PointNT, PointOutT >::computePointSPFHSignature().

◆ computePointNormal() [1/2]

template<typename PointT >

bool pcl::computePointNormal	(	const pcl::PointCloud< PointT > &	cloud,
		const pcl::Indices &	indices,
		Eigen::Vector4f &	plane_parameters,
		float &	curvature
	)

inline

#include <pcl/features/normal_3d.h>

Compute the Least-Squares plane fit for a given set of points, using their indices, and return the estimated plane parameters together with the surface curvature.

Parameters

cloud	the input point cloud
indices	the point cloud indices that need to be used
plane_parameters	the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
curvature	the estimated surface curvature as a measure of $\lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)$

Definition at line 94 of file normal_3d.h.

References pcl::computeMeanAndCovarianceMatrix(), and pcl::solvePlaneParameters().

◆ computePointNormal() [2/2]

template<typename PointT >

bool pcl::computePointNormal	(	const pcl::PointCloud< PointT > &	cloud,
		Eigen::Vector4f &	plane_parameters,
		float &	curvature
	)

inline

#include <pcl/features/normal_3d.h>

Compute the Least-Squares plane fit for a given set of points, and return the estimated plane parameters together with the surface curvature.

Parameters

cloud	the input point cloud
plane_parameters	the plane parameters as: a, b, c, d (ax + by + cz + d = 0)
curvature	the estimated surface curvature as a measure of $\lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)$

Definition at line 61 of file normal_3d.h.

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

Referenced by pcl::NormalEstimation< PointInT, PointOutT >::computeFeature(), pcl::IntegralImageNormalEstimation< PointInT, PointOutT >::computeFeatureFull(), and pcl::IntegralImageNormalEstimation< PointInT, PointOutT >::computeFeaturePart().

◆ computeRSD() [1/2]

template<typename PointInT , typename PointNT , typename PointOutT >

Eigen::MatrixXf pcl::computeRSD	(	const pcl::PointCloud< PointInT > &	surface,
		const pcl::PointCloud< PointNT > &	normals,
		const pcl::Indices &	indices,
		double	max_dist,
		int	nr_subdiv,
		double	plane_radius,
		PointOutT &	radii,
		bool	compute_histogram = `false`
	)

#include <pcl/features/rsd.h>

Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals.

Parameters

[in]	surface	the dataset containing the XYZ points
[in]	normals	the dataset containing the surface normals at each point in the dataset
[in]	indices	the neighborhood point indices in the dataset (first point is used as the reference)
[in]	max_dist	the upper bound for the considered distance interval
[in]	nr_subdiv	the number of subdivisions for the considered distance interval
[in]	plane_radius	maximum radius, above which everything can be considered planar
[in]	radii	the output point of a type that should have r_min and r_max fields
[in]	compute_histogram	if not false, the full neighborhood histogram is provided, usable as a point signature

Note: : orientation is neglected!; : we neglect points that are outside the specified interval!

Definition at line 49 of file rsd.hpp.

References M_PI.

Referenced by pcl::RSDEstimation< PointInT, PointNT, PointOutT >::computeFeature().

◆ computeRSD() [2/2]

template<typename PointNT , typename PointOutT >

Eigen::MatrixXf pcl::computeRSD	(	const pcl::PointCloud< PointNT > &	normals,
		const pcl::Indices &	indices,
		const std::vector< float > &	sqr_dists,
		double	max_dist,
		int	nr_subdiv,
		double	plane_radius,
		PointOutT &	radii,
		bool	compute_histogram = `false`
	)

#include <pcl/features/rsd.h>

Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals.

Parameters

[in]	normals	the dataset containing the surface normals at each point in the dataset
[in]	indices	the neighborhood point indices in the dataset (first point is used as the reference)
[in]	sqr_dists	the squared distances from the first to all points in the neighborhood
[in]	max_dist	the upper bound for the considered distance interval
[in]	nr_subdiv	the number of subdivisions for the considered distance interval
[in]	plane_radius	maximum radius, above which everything can be considered planar
[in]	radii	the output point of a type that should have r_min and r_max fields
[in]	compute_histogram	if not false, the full neighborhood histogram is provided, usable as a point signature

Note: : orientation is neglected!; : we neglect points that are outside the specified interval!

Definition at line 149 of file rsd.hpp.

References M_PI.

◆ flipNormalTowardsNormalsMean()

template<typename PointNT >

bool pcl::flipNormalTowardsNormalsMean	(	pcl::PointCloud< PointNT > const &	normal_cloud,
		pcl::Indices const &	normal_indices,
		Eigen::Vector3f &	normal
	)

inline

#include <pcl/features/normal_3d.h>

Flip (in place) normal to get the same sign of the mean of the normals specified by normal_indices.

The method is described in: A. Petrelli, L. Di Stefano, "A repeatable and efficient canonical reference for surface matching", 3DimPVT, 2012 A. Petrelli, L. Di Stefano, "On the repeatability of the local reference frame for partial shape matching", 13th International Conference on Computer Vision (ICCV), 2011

Normals should be unit vectors. Otherwise the resulting mean would be weighted by the normal norms.

Parameters

[in]	normal_cloud	Cloud of normals used to compute the mean
[in]	normal_indices	Indices of normals used to compute the mean
[in]	normal	input Normal to flip. Normal is modified by the function.

Returns: false if normal_indices does not contain any valid normal.

Definition at line 204 of file normal_3d.h.

References pcl::isFinite().

◆ flipNormalTowardsViewpoint() [1/3]

template<typename PointT , typename Scalar >

void pcl::flipNormalTowardsViewpoint	(	const PointT &	point,
		float	vp_x,
		float	vp_y,
		float	vp_z,
		Eigen::Matrix< Scalar, 3, 1 > &	normal
	)

inline

#include <pcl/features/normal_3d.h>

Flip (in place) the estimated normal of a point towards a given viewpoint.

Parameters

point	a given point
vp_x	the X coordinate of the viewpoint
vp_y	the X coordinate of the viewpoint
vp_z	the X coordinate of the viewpoint
normal	the plane normal to be flipped

Definition at line 149 of file normal_3d.h.

◆ flipNormalTowardsViewpoint() [2/3]

template<typename PointT , typename Scalar >

void pcl::flipNormalTowardsViewpoint	(	const PointT &	point,
		float	vp_x,
		float	vp_y,
		float	vp_z,
		Eigen::Matrix< Scalar, 4, 1 > &	normal
	)

inline

#include <pcl/features/normal_3d.h>

Flip (in place) the estimated normal of a point towards a given viewpoint.

Parameters

point	a given point
vp_x	the X coordinate of the viewpoint
vp_y	the X coordinate of the viewpoint
vp_z	the X coordinate of the viewpoint
normal	the plane normal to be flipped

Definition at line 122 of file normal_3d.h.

Referenced by pcl::features::computeApproximateNormals(), pcl::NormalEstimation< PointInT, PointOutT >::computeFeature(), pcl::IntegralImageNormalEstimation< PointInT, PointOutT >::computePointNormal(), and pcl::IntegralImageNormalEstimation< PointInT, PointOutT >::computePointNormalMirror().

◆ flipNormalTowardsViewpoint() [3/3]

template<typename PointT >

void pcl::flipNormalTowardsViewpoint	(	const PointT &	point,
		float	vp_x,
		float	vp_y,
		float	vp_z,
		float &	nx,
		float &	ny,
		float &	nz
	)

inline

#include <pcl/features/normal_3d.h>

Flip (in place) the estimated normal of a point towards a given viewpoint.

Parameters

point	a given point
vp_x	the X coordinate of the viewpoint
vp_y	the X coordinate of the viewpoint
vp_z	the X coordinate of the viewpoint
nx	the resultant X component of the plane normal
ny	the resultant Y component of the plane normal
nz	the resultant Z component of the plane normal

Definition at line 170 of file normal_3d.h.

◆ getFeaturePointCloud()

template<int N>

void pcl::getFeaturePointCloud	(	const std::vector< Eigen::MatrixXf, Eigen::aligned_allocator< Eigen::MatrixXf > > &	histograms2D,
		PointCloud< Histogram< N > > &	histogramsPC
	)

#include <pcl/features/rsd.h>

Transform a list of 2D matrices into a point cloud containing the values in a vector (Histogram<N>).

Can be used to transform the 2D histograms obtained in RSDEstimation into a point cloud.

Note: The template parameter N should be (greater or) equal to the product of the number of rows and columns.

Parameters

[in]	histograms2D	the list of neighborhood 2D histograms
[out]	histogramsPC	the dataset containing the linearized matrices

Definition at line 57 of file rsd.h.

References pcl::PointCloud< PointT >::begin().

◆ solvePlaneParameters() [1/2]

void pcl::solvePlaneParameters	(	const Eigen::Matrix3f &	covariance_matrix,
		const Eigen::Vector4f &	point,
		Eigen::Vector4f &	plane_parameters,
		float &	curvature
	)

inline

#include <pcl/features/feature.h>

Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.

Parameters

covariance_matrix	the 3x3 covariance matrix
point	a point lying on the least-squares plane (SSE aligned)
plane_parameters	the resultant plane parameters as: a, b, c, d (ax + by + cz + d = 0)
curvature	the estimated surface curvature as a measure of $\lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)$

Definition at line 52 of file feature.hpp.

Referenced by pcl::NormalEstimation< PointInT, PointOutT >::computePointNormal(), and pcl::computePointNormal().

◆ solvePlaneParameters() [2/2]

void pcl::solvePlaneParameters	(	const Eigen::Matrix3f &	covariance_matrix,
		float &	nx,
		float &	ny,
		float &	nz,
		float &	curvature
	)

inline

#include <pcl/features/feature.h>

Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares plane normal and surface curvature.

Parameters

covariance_matrix	the 3x3 covariance matrix
nx	the resultant X component of the plane normal
ny	the resultant Y component of the plane normal
nz	the resultant Z component of the plane normal
curvature	the estimated surface curvature as a measure of $\lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)$

Definition at line 65 of file feature.hpp.

References pcl::eigen33().

Detailed Description

Overview

Requirements

Classes

Functions

Function Documentation

◆ computePairFeatures()

◆ computePointNormal() [1/2]

◆ computePointNormal() [2/2]

◆ computeRSD() [1/2]

◆ computeRSD() [2/2]

◆ flipNormalTowardsNormalsMean()

◆ flipNormalTowardsViewpoint() [1/3]

◆ flipNormalTowardsViewpoint() [2/3]

◆ flipNormalTowardsViewpoint() [3/3]

◆ getFeaturePointCloud()

◆ solvePlaneParameters() [1/2]

◆ solvePlaneParameters() [2/2]