Class cv::ppf_match_3d::ICP#
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:
#include <opencv2/surface_matching/icp.hpp>Collaboration diagram for cv::ppf_match_3d::ICP:
Public Types#
enum cv::ppf_match_3d::ICP {
cv::ppf_match_3d::ICP_SAMPLING_TYPE_UNIFORM = 0,
cv::ppf_match_3d::ICP_SAMPLING_TYPE_GELFAND = 1
}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:
Picky ICP: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2003/Zinsser03-ARI.pdf
Efficient variants of the ICP Algorithm: http://docs.happycoders.org/orgadoc/graphics/imaging/fasticp_paper.pdf
Geometrically Stable Sampling for the ICP Algorithm: https://graphics.stanford.edu/papers/stabicp/stabicp.pdf
Multi-resolution registration: http://www.cvl.iis.u-tokyo.ac.jp/~oishi/Papers/Alignment/Jost_MultiResolutionICP_3DIM03.pdf
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#
enum ICP
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Constructor & Destructor Documentation#
ICP()#
Python:
cv.ppf_match_3d.ICP(iterations[, tolerence[, rejectionScale[, numLevels[, sampleType[, numMaxCorr]]]]]) -> <ppf_match_3d_ICP object>
ICP()#
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 )
Python:
cv.ppf_match_3d.ICP(iterations[, tolerence[, rejectionScale[, numLevels[, sampleType[, numMaxCorr]]]]]) -> <ppf_match_3d_ICP object>
ICP constructor with default arguments.
Parameters
iterationstolerence— Controls the accuracy of registration at each iteration of ICP.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.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.sampleType— Currently this parameter is ignored and only uniform sampling is applied. Leave it as 0.numMaxCorr— Currently this parameter is ignored and only PickyICP is applied. Leave it as 1.
~ICP()#
Member Function Documentation#
registerModelToScene()#
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, poses) -> retval, poses
Perform registration.
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).
Parameters
srcPC— The input point cloud for the model. Expected to have the normals (Nx6). Currently, CV_32F is the only supported data type.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.residual— The output registration error.pose— Transformation between srcPC and dstPC.
Returns
On successful termination, the function returns 0.
registerModelToScene()#
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, poses) -> retval, poses
Perform registration with multiple initial poses.
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).
Parameters
srcPC— The input point cloud for the model. Expected to have the normals (Nx6). Currently, CV_32F is the only supported data type.dstPC— The input point cloud for the scene. Currently, CV_32F is the only supported data type.poses— Input poses to start with but also list output of poses.
Returns
On successful termination, the function returns 0.
Member Data Documentation#
m_maxIterations#
int cv::ppf_match_3d::ICP::m_maxIterations
m_numLevels#
int cv::ppf_match_3d::ICP::m_numLevels
m_numNeighborsCorr#
int cv::ppf_match_3d::ICP::m_numNeighborsCorr
m_rejectionScale#
float cv::ppf_match_3d::ICP::m_rejectionScale
m_sampleType#
int cv::ppf_match_3d::ICP::m_sampleType
m_tolerance#
float cv::ppf_match_3d::ICP::m_tolerance
Source file#
The documentation for this class was generated from the following file:
opencv2/surface_matching/icp.hpp