Classes | Functions

Module features


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.

features_normal.png

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 efficienctly, 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 vecinity 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 $\lambda_0 < \lambda_1 < \lambda_2$.
features_bunny.png

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

History

Requirements

Classes

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::CVFHEstimation< PointInT, PointNT, PointOutT >
 CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing points and normals. More...
class  pcl::Feature< PointInT, PointOutT >
 Feature represents the base feature class. 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::IntegralImage2D< DataType, IIDataType >
 Generic implementation for creating 2D integral images (including second order integral images). More...
class  pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT >
 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 More...
class  pcl::NormalEstimation< PointInT, PointOutT >
 NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures. 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::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::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 >
 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 >
 SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals. More...
class  pcl::SHOTEstimation< pcl::PointXYZRGBA, PointNT, PointOutT >
 SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals. More...
class  pcl::SHOTEstimationOMP< PointInT, PointNT, PointOutT >
 SHOTEstimation 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::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.
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.
template<typename PointT >
void 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.
template<typename PointT >
void pcl::computePointNormal (const pcl::PointCloud< PointT > &cloud, const std::vector< int > &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.
template<typename PointT >
void pcl::flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z, Eigen::Vector4f &normal)
 Flip (in place) the estimated normal of a point towards a given viewpoint.
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.
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.
template<typename PointInT , typename PointNT , typename PointOutT >
void pcl::computeRSD (const pcl::PointCloud< PointInT > &surface, const pcl::PointCloud< PointNT > &normals, const std::vector< int > &indices, double max_dist, int nr_subdiv, double plane_radius, PointOutT &radii)
 Estimate the Radius-based Surface Descriptor (RSD) for a given point based on its spatial neighborhood of 3D points with normals.

Function Documentation

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.

Note:
For explanations about the features, please see the literature mentioned above (the order of the features might be different).
Parameters:
p1 the first XYZ point
n1 the first surface normal
p2 the second XYZ point
n2 the second surface normal
f1 the first angular feature (angle between the projection of nq_idx and u)
f2 the second angular feature (angle between nq_idx and v)
f3 the third angular feature (angle between np_idx and |p_idx - q_idx|)
f4 the distance feature (p_idx - q_idx)
template<typename PointT >
void pcl::computePointNormal ( const pcl::PointCloud< PointT > &  cloud,
const std::vector< int > &  indices,
Eigen::Vector4f &  plane_parameters,
float &  curvature 
) [inline]

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 92 of file normal_3d.h.

template<typename PointT >
void pcl::computePointNormal ( const pcl::PointCloud< PointT > &  cloud,
Eigen::Vector4f &  plane_parameters,
float &  curvature 
) [inline]

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 56 of file normal_3d.h.

template<typename PointInT , typename PointNT , typename PointOutT >
void pcl::computeRSD ( const pcl::PointCloud< PointInT > &  surface,
const pcl::PointCloud< PointNT > &  normals,
const std::vector< int > &  indices,
double  max_dist,
int  nr_subdiv,
double  plane_radius,
PointOutT &  radii 
) [inline]

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

Parameters:
surface the dataset containing the XYZ points
normals the dataset containing the surface normals at each point in the dataset
indices the neighborhood point indices in the dataset
max_dist the upper bound for the considered distance interval
nr_subdiv the number of subdivisions for the considered distance interval
plane_radius document me
radii the output point of a type that should have r_min and r_max fields

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

Definition at line 46 of file rsd.hpp.

template<typename PointT >
void pcl::flipNormalTowardsViewpoint ( const PointT &  point,
float  vp_x,
float  vp_y,
float  vp_z,
Eigen::Vector4f &  normal 
) [inline]

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 125 of file normal_3d.h.

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]

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 157 of file normal_3d.h.

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

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 93 of file feature.hpp.

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

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 43 of file feature.hpp.