cv::ppf_match_3d::ICP Class Reference

This class implements a very efficient and robust variant of the iterative closest point (ICP) algorithm. The task is to register a 3D model (or point cloud) against a set of noisy target data. The variants are put together by myself after certain tests. The task is to be able to match partial, noisy point clouds in cluttered scenes, quickly. You will find that my emphasis is on the performance, while retaining the accuracy. This implementation is based on Tolga Birdal's MATLAB implementation in here: http://www.mathworks.com/matlabcentral/fileexchange/47152-icp-registration-using-efficient-variants-and-multi-resolution-scheme The main contributions come from: More...

#include <opencv2/surface_matching/icp.hpp>

Collaboration diagram for cv::ppf_match_3d::ICP:

Public Types
enum	{   ICP_SAMPLING_TYPE_UNIFORM = 0 ,   ICP_SAMPLING_TYPE_GELFAND = 1 }

Public Member Functions
	ICP ()
	ICP (const int iterations, const float tolerence=0.05f, const float rejectionScale=2.5f, const int numLevels=6, const int sampleType=ICP::ICP_SAMPLING_TYPE_UNIFORM, const int numMaxCorr=1)
	ICP constructor with default arguments.
virtual	~ICP ()
int	registerModelToScene (const Mat &srcPC, const Mat &dstPC, double &residual, Matx44d &pose)
	Perform registration.
int	registerModelToScene (const Mat &srcPC, const Mat &dstPC, std::vector< Pose3DPtr > &poses)
	Perform registration with multiple initial poses.

Detailed Description

This class implements a very efficient and robust variant of the iterative closest point (ICP) algorithm. The task is to register a 3D model (or point cloud) against a set of noisy target data. The variants are put together by myself after certain tests. The task is to be able to match partial, noisy point clouds in cluttered scenes, quickly. You will find that my emphasis is on the performance, while retaining the accuracy. This implementation is based on Tolga Birdal's MATLAB implementation in here: http://www.mathworks.com/matlabcentral/fileexchange/47152-icp-registration-using-efficient-variants-and-multi-resolution-scheme The main contributions come from:

1. Picky ICP: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2003/Zinsser03-ARI.pdf
2. Efficient variants of the ICP Algorithm: http://docs.happycoders.org/orgadoc/graphics/imaging/fasticp_paper.pdf
3. Geometrically Stable Sampling for the ICP Algorithm: https://graphics.stanford.edu/papers/stabicp/stabicp.pdf
4. Multi-resolution registration: http://www.cvl.iis.u-tokyo.ac.jp/~oishi/Papers/Alignment/Jost_MultiResolutionICP_3DIM03.pdf
5. Linearization of Point-to-Plane metric by Kok Lim Low: https://www.comp.nus.edu.sg/~lowkl/publications/lowk_point-to-plane_icp_techrep.pdf

Member Enumeration Documentation

anonymous enum

anonymous enum

Enumerator
ICP_SAMPLING_TYPE_UNIFORM
ICP_SAMPLING_TYPE_GELFAND

Constructor & Destructor Documentation

ICP() [1/2]

cv::ppf_match_3d::ICP::ICP ( )

inline

Python:
	cv.ppf_match_3d.ICP(		) ->	<ppf_match_3d_ICP object>
	cv.ppf_match_3d.ICP(	iterations[, tolerence[, rejectionScale[, numLevels[, sampleType[, numMaxCorr]]]]]	) ->	<ppf_match_3d_ICP object>

~ICP()

virtual cv::ppf_match_3d::ICP::~ICP ( )

inlinevirtual

ICP() [2/2]

cv::ppf_match_3d::ICP::ICP ( const int iterations, const float tolerence = 0.05f, const float rejectionScale = 2.5f, const int numLevels = 6, const int sampleType = ICP::ICP_SAMPLING_TYPE_UNIFORM, const int numMaxCorr = 1 )

inline

Python:
	cv.ppf_match_3d.ICP(		) ->	<ppf_match_3d_ICP object>
	cv.ppf_match_3d.ICP(	iterations[, tolerence[, rejectionScale[, numLevels[, sampleType[, numMaxCorr]]]]]	) ->	<ppf_match_3d_ICP object>

ICP constructor with default arguments.

Parameters

[in]	iterations
[in]	tolerence	Controls the accuracy of registration at each iteration of ICP.
[in]	rejectionScale	Robust outlier rejection is applied for robustness. This value actually corresponds to the standard deviation coefficient. Points with rejectionScale * &sigma are ignored during registration.
[in]	numLevels	Number of pyramid levels to proceed. Deep pyramids increase speed but decrease accuracy. Too coarse pyramids might have computational overhead on top of the inaccurate registrtaion. This parameter should be chosen to optimize a balance. Typical values range from 4 to 10.
[in]	sampleType	Currently this parameter is ignored and only uniform sampling is applied. Leave it as 0.
[in]	numMaxCorr	Currently this parameter is ignored and only PickyICP is applied. Leave it as 1.

Member Function Documentation

registerModelToScene() [1/2]

int cv::ppf_match_3d::ICP::registerModelToScene	(	const Mat &	srcPC,
		const Mat &	dstPC,
		double &	residual,
		Matx44d &	pose )

Python:
	cv.ppf_match_3d.ICP.registerModelToScene(	srcPC, dstPC	) ->	retval, residual, pose
	cv.ppf_match_3d.ICP.registerModelToScene(	srcPC, dstPC, poses	) ->	retval, poses

Perform registration.

Parameters

[in]	srcPC	The input point cloud for the model. Expected to have the normals (Nx6). Currently, CV_32F is the only supported data type.
[in]	dstPC	The input point cloud for the scene. It is assumed that the model is registered on the scene. Scene remains static. Expected to have the normals (Nx6). Currently, CV_32F is the only supported data type.
[out]	residual	The output registration error.
[out]	pose	Transformation between srcPC and dstPC.

Returns

On successful termination, the function returns 0.

It is assumed that the model is registered on the scene. Scene remains static, while the model transforms. The output poses transform the models onto the scene. Because of the point to plane minimization, the scene is expected to have the normals available. Expected to have the normals (Nx6).

registerModelToScene() [2/2]

int cv::ppf_match_3d::ICP::registerModelToScene	(	const Mat &	srcPC,
		const Mat &	dstPC,
		std::vector< Pose3DPtr > &	poses )

Python:
	cv.ppf_match_3d.ICP.registerModelToScene(	srcPC, dstPC	) ->	retval, residual, pose
	cv.ppf_match_3d.ICP.registerModelToScene(	srcPC, dstPC, poses	) ->	retval, poses

Perform registration with multiple initial poses.

Parameters

[in]	srcPC	The input point cloud for the model. Expected to have the normals (Nx6). Currently, CV_32F is the only supported data type.
[in]	dstPC	The input point cloud for the scene. Currently, CV_32F is the only supported data type.
[in,out]	poses	Input poses to start with but also list output of poses.

Returns

On successful termination, the function returns 0.

It is assumed that the model is registered on the scene. Scene remains static, while the model transforms. The output poses transform the models onto the scene. Because of the point to plane minimization, the scene is expected to have the normals available. Expected to have the normals (Nx6).

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

• opencv2/surface_matching/icp.hpp