Point Cloud Library (PCL)  1.3.1
Public Types | Public Member Functions
pcl::IntegralImageNormalEstimation Class Reference

Surface normal estimation on dense data using integral images. More...

#include <pcl/features/integral_image_normal.h>

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List of all members.

Public Types

enum  NormalEstimationMethod { COVARIANCE_MATRIX, AVERAGE_3D_GRADIENT, AVERAGE_DEPTH_CHANGE }
typedef Feature< PointInT,
PointOutT >::PointCloudIn 
PointCloudIn
typedef Feature< PointInT,
PointOutT >::PointCloudOut 
PointCloudOut
typedef PCLBase< PointInT > BaseClass
typedef boost::shared_ptr
< Feature< PointInT, PointOutT > > 
Ptr
typedef boost::shared_ptr
< const Feature< PointInT,
PointOutT > > 
ConstPtr
typedef pcl::search::Search
< PointInT > 
KdTree
typedef pcl::search::Search
< PointInT >::Ptr 
KdTreePtr
typedef PointCloudIn::Ptr PointCloudInPtr
typedef PointCloudIn::ConstPtr PointCloudInConstPtr
typedef boost::function< int(size_t,
double, std::vector< int >
&, std::vector< float > &)> 
SearchMethod
typedef boost::function< int(const
PointCloudIn &cloud, size_t
index, double, std::vector
< int > &, std::vector< float > &)> 
SearchMethodSurface

Public Member Functions

 IntegralImageNormalEstimation ()
 Constructor.
virtual ~IntegralImageNormalEstimation ()
 Destructor.
void setRectSize (const int width, const int height)
 Set the regions size which is considered for normal estimation.
void computePointNormal (const int pos_x, const int pos_y, PointOutT &normal)
 Computes the normal at the specified position.
void setMaxDepthChangeFactor (float max_depth_change_factor)
 The depth change threshold for computing object borders.
void setNormalSmoothingSize (float normal_smoothing_size)
 Set the normal smoothing size.
void setNormalEstimationMethod (NormalEstimationMethod normal_estimation_method)
 Set the normal estimation method.
void setDepthDependentSmoothing (bool use_depth_dependent_smoothing)
 Set whether to use depth depending smoothing or not.
virtual void setInputCloud (const typename PointCloudIn::ConstPtr &cloud)
 Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
void setSearchSurface (const PointCloudInConstPtr &cloud)
 Provide a pointer to a dataset to add additional information to estimate the features for every point in the input dataset.
PointCloudInConstPtr getSearchSurface ()
 Get a pointer to the surface point cloud dataset.
void setSearchMethod (const KdTreePtr &tree)
 Provide a pointer to the search object.
KdTreePtr getSearchMethod ()
 Get a pointer to the search method used.
double getSearchParameter ()
 Get the internal search parameter.
void setKSearch (int k)
 Set the number of k nearest neighbors to use for the feature estimation.
int getKSearch ()
 get the number of k nearest neighbors used for the feature estimation.
void setRadiusSearch (double radius)
 Set the sphere radius that is to be used for determining the nearest neighbors used for the feature estimation.
double getRadiusSearch ()
 Get the sphere radius used for determining the neighbors.
void compute (PointCloudOut &output)
 Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in setSearchMethod ()
int searchForNeighbors (size_t index, double parameter, std::vector< int > &indices, std::vector< float > &distances) const
 Search for k-nearest neighbors using the spatial locator from setSearchmethod, and the given surface from setSearchSurface.
int searchForNeighbors (const PointCloudIn &cloud, size_t index, double parameter, std::vector< int > &indices, std::vector< float > &distances) const
 Search for k-nearest neighbors using the spatial locator from setSearchmethod, and the given surface from setSearchSurface.

Detailed Description

Surface normal estimation on dense data using integral images.

Author:
Stefan Holzer

Member Typedef Documentation

typedef PCLBase<PointInT> pcl::Feature::BaseClass [inherited]

Definition at line 103 of file feature.h.

typedef boost::shared_ptr< const Feature<PointInT, PointOutT> > pcl::Feature::ConstPtr [inherited]

Reimplemented in pcl::FeatureFromNormals.

Definition at line 106 of file feature.h.

typedef pcl::search::Search<PointInT> pcl::Feature::KdTree [inherited]

Definition at line 108 of file feature.h.

typedef pcl::search::Search<PointInT>::Ptr pcl::Feature::KdTreePtr [inherited]

Definition at line 109 of file feature.h.

Reimplemented from pcl::Feature< PointInT, PointOutT >.

Definition at line 70 of file integral_image_normal.h.

typedef PointCloudIn::ConstPtr pcl::Feature::PointCloudInConstPtr [inherited]

Definition at line 113 of file feature.h.

typedef PointCloudIn::Ptr pcl::Feature::PointCloudInPtr [inherited]

Definition at line 112 of file feature.h.

Reimplemented from pcl::Feature< PointInT, PointOutT >.

Definition at line 71 of file integral_image_normal.h.

typedef boost::shared_ptr< Feature<PointInT, PointOutT> > pcl::Feature::Ptr [inherited]

Reimplemented in pcl::FeatureFromNormals.

Definition at line 105 of file feature.h.

typedef boost::function<int (size_t, double, std::vector<int> &, std::vector<float> &)> pcl::Feature::SearchMethod [inherited]

Definition at line 117 of file feature.h.

typedef boost::function<int (const PointCloudIn &cloud, size_t index, double, std::vector<int> &, std::vector<float> &)> pcl::Feature::SearchMethodSurface [inherited]

Definition at line 118 of file feature.h.


Member Enumeration Documentation

Enumerator:
COVARIANCE_MATRIX 
AVERAGE_3D_GRADIENT 
AVERAGE_DEPTH_CHANGE 

Definition at line 63 of file integral_image_normal.h.


Constructor & Destructor Documentation

pcl::IntegralImageNormalEstimation::IntegralImageNormalEstimation ( ) [inline]

Constructor.

Definition at line 74 of file integral_image_normal.h.

pcl::IntegralImageNormalEstimation::~IntegralImageNormalEstimation ( ) [virtual]

Destructor.

Definition at line 44 of file integral_image_normal.hpp.


Member Function Documentation

void pcl::Feature::compute ( PointCloudOut output) [inherited]

Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in setSearchMethod ()

Parameters:
outputthe resultant point cloud model dataset containing the estimated features
void pcl::IntegralImageNormalEstimation::computePointNormal ( const int  pos_x,
const int  pos_y,
PointOutT &  normal 
)

Computes the normal at the specified position.

Parameters:
pos_xx position (pixel)
pos_yy position (pixel)
normalthe output estimated normal

Definition at line 181 of file integral_image_normal.hpp.

int pcl::Feature::getKSearch ( ) [inline, inherited]

get the number of k nearest neighbors used for the feature estimation.

Definition at line 166 of file feature.h.

double pcl::Feature::getRadiusSearch ( ) [inline, inherited]

Get the sphere radius used for determining the neighbors.

Definition at line 177 of file feature.h.

KdTreePtr pcl::Feature::getSearchMethod ( ) [inline, inherited]

Get a pointer to the search method used.

Definition at line 152 of file feature.h.

double pcl::Feature::getSearchParameter ( ) [inline, inherited]

Get the internal search parameter.

Definition at line 156 of file feature.h.

PointCloudInConstPtr pcl::Feature::getSearchSurface ( ) [inline, inherited]

Get a pointer to the surface point cloud dataset.

Definition at line 142 of file feature.h.

int pcl::Feature::searchForNeighbors ( size_t  index,
double  parameter,
std::vector< int > &  indices,
std::vector< float > &  distances 
) const [inline, inherited]

Search for k-nearest neighbors using the spatial locator from setSearchmethod, and the given surface from setSearchSurface.

Parameters:
indexthe index of the query point
parameterthe search parameter (either k or radius)
indicesthe resultant vector of indices representing the k-nearest neighbors
distancesthe resultant vector of distances representing the distances from the query point to the k-nearest neighbors

Definition at line 196 of file feature.h.

int pcl::Feature::searchForNeighbors ( const PointCloudIn cloud,
size_t  index,
double  parameter,
std::vector< int > &  indices,
std::vector< float > &  distances 
) const [inline, inherited]

Search for k-nearest neighbors using the spatial locator from setSearchmethod, and the given surface from setSearchSurface.

Parameters:
cloudthe query point cloud
indexthe index of the query point in cloud
parameterthe search parameter (either k or radius)
indicesthe resultant vector of indices representing the k-nearest neighbors
distancesthe resultant vector of distances representing the distances from the query point to the k-nearest neighbors

Definition at line 215 of file feature.h.

void pcl::IntegralImageNormalEstimation::setDepthDependentSmoothing ( bool  use_depth_dependent_smoothing) [inline]

Set whether to use depth depending smoothing or not.

Parameters:
use_depth_dependent_smoothingdecides whether the smoothing is depth dependent

Definition at line 151 of file integral_image_normal.h.

virtual void pcl::IntegralImageNormalEstimation::setInputCloud ( const typename PointCloudIn::ConstPtr &  cloud) [inline, virtual]

Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)

Parameters:
cloudthe const boost shared pointer to a PointCloud message

Definition at line 160 of file integral_image_normal.h.

void pcl::Feature::setKSearch ( int  k) [inline, inherited]

Set the number of k nearest neighbors to use for the feature estimation.

Parameters:
kthe number of k-nearest neighbors

Definition at line 162 of file feature.h.

void pcl::IntegralImageNormalEstimation::setMaxDepthChangeFactor ( float  max_depth_change_factor) [inline]

The depth change threshold for computing object borders.

Parameters:
max_depth_change_factorthe depth change threshold for computing object borders based on depth changes

Definition at line 114 of file integral_image_normal.h.

void pcl::IntegralImageNormalEstimation::setNormalEstimationMethod ( NormalEstimationMethod  normal_estimation_method) [inline]

Set the normal estimation method.

The current implemented algorithms are:

  • COVARIANCE_MATRIX - creates 9 integral images to compute the normal for a specific point from the covariance matrix of its local neighborhood.
  • AVERAGE_3D_GRADIENT - creates 6 integral images to compute smoothed versions of horizontal and vertical 3D gradients and computes the normals using the cross-product between these two gradients.
  • AVERAGE_DEPTH_CHANGE - creates only a single integral image and computes the normals from the average depth changes.
Parameters:
normal_estimation_methodthe method used for normal estimation

Definition at line 142 of file integral_image_normal.h.

void pcl::IntegralImageNormalEstimation::setNormalSmoothingSize ( float  normal_smoothing_size) [inline]

Set the normal smoothing size.

Parameters:
normal_smoothing_sizefactor which influences the size of the area used to smooth normals (depth dependent if useDepthDependentSmoothing is true)

Definition at line 124 of file integral_image_normal.h.

void pcl::Feature::setRadiusSearch ( double  radius) [inline, inherited]

Set the sphere radius that is to be used for determining the nearest neighbors used for the feature estimation.

Parameters:
radiusthe sphere radius used as the maximum distance to consider a point a neighbor

Definition at line 173 of file feature.h.

void pcl::IntegralImageNormalEstimation::setRectSize ( const int  width,
const int  height 
)

Set the regions size which is considered for normal estimation.

Parameters:
widththe width of the search rectangle
heightthe height of the search rectangle

Definition at line 79 of file integral_image_normal.hpp.

void pcl::Feature::setSearchMethod ( const KdTreePtr tree) [inline, inherited]

Provide a pointer to the search object.

Parameters:
treea pointer to the spatial search object.

Definition at line 148 of file feature.h.

void pcl::Feature::setSearchSurface ( const PointCloudInConstPtr cloud) [inline, inherited]

Provide a pointer to a dataset to add additional information to estimate the features for every point in the input dataset.

This is optional, if this is not set, it will only use the data in the input cloud to estimate the features. This is useful when you only need to compute the features for a downsampled cloud.

Parameters:
clouda pointer to a PointCloud message

Definition at line 133 of file feature.h.


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