MatchingResult Class Reference

Results of a matching and down sampling operation. More...

#include <cvb/match_3d/match_3d.hpp>

Public Member Functions
double	DistanceRMS () const noexcept
	Get the final root mean square of distances between model and scene. More...
AffineMatrix3D	Transformation () const noexcept
	Get the affine transformation (rotation matrix and translation vector), which maps the scene to the model. More...
int	NumIterations () const noexcept
	Get number of iterations needed. More...

Friends
MatchingResult	MatchDownsampledPointClouds (const PointCloud &model, const PointCloud &scene, const MatchingParameters &parameters)
	This function matches two point clouds. More...

Detailed Description

Results of a matching and down sampling operation.

Member Function Documentation

DistanceRMS()

double DistanceRMS ( ) const

inlinenoexcept

Get the final root mean square of distances between model and scene.

Returns

The root mean square.

Exceptions

Does	not throw any exception.

NumIterations()

int NumIterations ( ) const

inlinenoexcept

Get number of iterations needed.

Returns

The number of iterations.

Exceptions

Does	not throw any exception.

Transformation()

AffineMatrix3D Transformation ( ) const

inlinenoexcept

Get the affine transformation (rotation matrix and translation vector), which maps the scene to the model.

Returns

The transformation.

Exceptions

Does	not throw any exception.

Friends And Related Function Documentation

MatchDownsampledPointClouds

MatchingResult MatchDownsampledPointClouds ( const PointCloud &  model, const PointCloud &  scene, const MatchingParameters &  parameters  )

friend

This function matches two point clouds.

Parameters

[in]	model	First point cloud.
[in]	scene	Second point cloud to be matched to model.
[in]	parameters	Parameters that control the algorithm.

Returns

Object containing all results.

Exceptions

Any	exception derived from std::exception including CvbException.

This function matches two point clouds by the iterative closest point algorithm (ICP), see: The algorithm follows Arun KS, Huang TS, Blostein SD(1987) Least-squares fitting of two 3D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence 9:698-700.