Normal vector refinement class More...

#include <normal_refinement.h>

Inheritance diagram for pcl::NormalRefinement< NormalT >:

Public Member Functions
float	getConvergenceThreshold ()
	Get convergence threshold.
void	getCorrespondences (std::vector< std::vector< int > > &k_indices, std::vector< std::vector< float > > &k_sqr_distances)
	Get correspondences (copy)
unsigned int	getMaxIterations ()
	Get maximum iterations.
	NormalRefinement ()
	Empty constructor, sets default convergence parameters.
	NormalRefinement (const std::vector< std::vector< int > > &k_indices, const std::vector< std::vector< float > > &k_sqr_distances)
	Constructor for setting correspondences, sets default convergence parameters.
void	setConvergenceThreshold (float convergence_threshold)
	Set convergence threshold.
void	setCorrespondences (const std::vector< std::vector< int > > &k_indices, const std::vector< std::vector< float > > &k_sqr_distances)
	Set correspondences calculated from nearest neighbor search.
void	setMaxIterations (unsigned int max_iterations)
	Set maximum iterations.
Protected Member Functions
void	applyFilter (PointCloud &output)
	Filter a Point Cloud.
Private Types
typedef Filter< NormalT > ::PointCloud	PointCloud
typedef PointCloud::ConstPtr	PointCloudConstPtr
typedef PointCloud::Ptr	PointCloudPtr
Private Attributes
float	convergence_threshold_
	Convergence threshold in the interval [0,1] on the mean of 1 - dot products between previous iteration and the current.
std::vector< std::vector< int > >	k_indices_
	indices of neighboring points
std::vector< std::vector< float > >	k_sqr_distances_
	squared distances to the neighboring points
unsigned int	max_iterations_
	Maximum allowable iterations over the whole point cloud for refinement.

Detailed Description

template<typename NormalT>
class pcl::NormalRefinement< NormalT >

Normal vector refinement class

This class refines a set of already estimated normals by iteratively updating each normal to the (weighted) mean of all normals in its neighborhood. The intention is that you reuse the same point correspondences as used when estimating the original normals in order to avoid repeating a nearest neighbor search.

Note:: This class avoids points for which a NaN is encountered in the neighborhood. In the special case where a point has only NaNs in its neighborhood, the resultant refined normal will be set to zero, i.e. this class only produces finite normals.

Usage example:

 // Input point cloud
 pcl::PointCloud<PointT> cloud;
 
 // Fill cloud...
 
 // Estimated and refined normals
 pcl::PointCloud<NormalT> normals;
 pcl::PointCloud<NormalT> normals_refined;
 
 // Search parameters
 const int k = 5;
 std::vector<std::vector<int> > k_indices;
 std::vector<std::vector<float> > k_sqr_distances;
 
 // Run search
 pcl::search::KdTree<pcl::PointXYZRGB> search;
 search.setInputCloud (cloud.makeShared ());
 search.nearestKSearch (cloud, std::vector<int> (), k, k_indices, k_sqr_distances);
 
 // Use search results for normal estimation
 pcl::NormalEstimation<PointT, NormalT> ne;
 for (unsigned int i = 0; i < cloud.size (); ++i)
 {
   NormalT normal;
   ne.computePointNormal (cloud, k_indices[i]
                          normal.normal_x, normal.normal_y, normal.normal_z, normal.curvature);
   pcl::flipNormalTowardsViewpoint (cloud[i], cloud.sensor_origin_[0], cloud.sensor_origin_[1], cloud.sensor_origin_[2],
                                    normal.normal_x, normal.normal_y, normal.normal_z);
   normals.push_back (normal);
 }
 
 // Run refinement using search results
 pcl::NormalRefinement<NormalT> nr (k_indices, k_sqr_distances);
 nr.setInputCloud (normals.makeShared ());
 nr.filter (normals_refined);

Author:: Anders Glent Buch

Definition at line 188 of file normal_refinement.h.

Member Typedef Documentation

template<typename NormalT>

typedef Filter<NormalT>::PointCloud pcl::NormalRefinement< NormalT >::PointCloud [private]

Reimplemented from pcl::Filter< NormalT >.

Definition at line 194 of file normal_refinement.h.

template<typename NormalT>

typedef PointCloud::ConstPtr pcl::NormalRefinement< NormalT >::PointCloudConstPtr [private]

Reimplemented from pcl::Filter< NormalT >.

Definition at line 196 of file normal_refinement.h.

template<typename NormalT>

typedef PointCloud::Ptr pcl::NormalRefinement< NormalT >::PointCloudPtr [private]

Reimplemented from pcl::Filter< NormalT >.

Definition at line 195 of file normal_refinement.h.

Constructor & Destructor Documentation

template<typename NormalT>

pcl::NormalRefinement< NormalT >::NormalRefinement ( ) [inline]

Empty constructor, sets default convergence parameters.

Definition at line 201 of file normal_refinement.h.

template<typename NormalT>

pcl::NormalRefinement< NormalT >::NormalRefinement	(	const std::vector< std::vector< int > > &	k_indices,
		const std::vector< std::vector< float > > &	k_sqr_distances
	)		`[inline]`

Constructor for setting correspondences, sets default convergence parameters.

Parameters:

k_indices	indices of neighboring points
k_sqr_distances	squared distances to the neighboring points

Definition at line 213 of file normal_refinement.h.

Member Function Documentation

template<typename NormalT >

void pcl::NormalRefinement< NormalT >::applyFilter ( PointCloud & output ) [protected]

Filter a Point Cloud.

Parameters:

output the resultant point cloud message

Definition at line 48 of file normal_refinement.hpp.

template<typename NormalT>

float pcl::NormalRefinement< NormalT >::getConvergenceThreshold ( ) [inline]

Get convergence threshold.

Returns:: convergence threshold

Definition at line 275 of file normal_refinement.h.

template<typename NormalT>

void pcl::NormalRefinement< NormalT >::getCorrespondences	(	std::vector< std::vector< int > > &	k_indices,
		std::vector< std::vector< float > > &	k_sqr_distances
	)		`[inline]`

Get correspondences (copy)

Parameters:

k_indices	indices of neighboring points
k_sqr_distances	squared distances to the neighboring points

Definition at line 238 of file normal_refinement.h.

template<typename NormalT>

unsigned int pcl::NormalRefinement< NormalT >::getMaxIterations ( ) [inline]

Get maximum iterations.

Returns:: maximum iterations

Definition at line 257 of file normal_refinement.h.

template<typename NormalT>

void pcl::NormalRefinement< NormalT >::setConvergenceThreshold ( float convergence_threshold ) [inline]

Set convergence threshold.

Parameters:

convergence_threshold convergence threshold

Definition at line 266 of file normal_refinement.h.

template<typename NormalT>

void pcl::NormalRefinement< NormalT >::setCorrespondences	(	const std::vector< std::vector< int > > &	k_indices,
		const std::vector< std::vector< float > > &	k_sqr_distances
	)		`[inline]`

Set correspondences calculated from nearest neighbor search.

Parameters:

k_indices	indices of neighboring points
k_sqr_distances	squared distances to the neighboring points

Definition at line 227 of file normal_refinement.h.

template<typename NormalT>

void pcl::NormalRefinement< NormalT >::setMaxIterations ( unsigned int max_iterations ) [inline]

Set maximum iterations.

Parameters:

max_iterations maximum iterations

Definition at line 248 of file normal_refinement.h.

Member Data Documentation

template<typename NormalT>

float pcl::NormalRefinement< NormalT >::convergence_threshold_ [private]

Convergence threshold in the interval [0,1] on the mean of 1 - dot products between previous iteration and the current.

Definition at line 298 of file normal_refinement.h.

template<typename NormalT>

std::vector< std::vector<int> > pcl::NormalRefinement< NormalT >::k_indices_ [private]

indices of neighboring points

Definition at line 289 of file normal_refinement.h.

template<typename NormalT>

std::vector< std::vector<float> > pcl::NormalRefinement< NormalT >::k_sqr_distances_ [private]

squared distances to the neighboring points

Definition at line 292 of file normal_refinement.h.

template<typename NormalT>

unsigned int pcl::NormalRefinement< NormalT >::max_iterations_ [private]

Maximum allowable iterations over the whole point cloud for refinement.

Definition at line 295 of file normal_refinement.h.

The documentation for this class was generated from the following files:

Public Member Functions

Protected Member Functions

Private Types

Private Attributes

Detailed Description

template<typename NormalT> class pcl::NormalRefinement< NormalT >

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<typename NormalT>
class pcl::NormalRefinement< NormalT >