CVB++ 14.0
MatchingResult Class Referencefinal

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
Doesnot throw any exception.

◆ NumIterations()

int NumIterations ( ) const
inlinenoexcept

Get number of iterations needed.

Returns
The number of iterations.
Exceptions
Doesnot 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
Doesnot 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]modelFirst point cloud.
[in]sceneSecond point cloud to be matched to model.
[in]parametersParameters that control the algorithm.
Returns
Object containing all results.
Exceptions
Anyexception 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.