cv::ppf_match_3d Namespace Reference

Classes
class	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: http://www.mathworks.com/matlabcentral/fileexchange/47152-icp-registration-using-efficient-variants-and-multi-resolution-scheme The main contributions come from: More...
class	Pose3D
	Class, allowing the storage of a pose. The data structure stores both the quaternions and the matrix forms. It supports IO functionality together with various helper methods to work with poses. More...
class	PoseCluster3D
	When multiple poses (see Pose3D) are grouped together (contribute to the same transformation) pose clusters occur. This class is a general container for such groups of poses. It is possible to store, load and perform IO on these poses. More...
class	PPF3DDetector
	Class, allowing the load and matching 3D models. Typical Use: More...

Typedefs
typedef unsigned int	KeyType
typedef Ptr< Pose3D >	Pose3DPtr
typedef Ptr< PoseCluster3D >	PoseCluster3DPtr

Functions
Mat	addNoisePC (Mat pc, double scale)
void	computeBboxStd (Mat pc, float xRange[2], float yRange[2], float zRange[2])
int	computeNormalsPC3d (const Mat &PC, Mat &PCNormals, const int NumNeighbors, const bool FlipViewpoint, const Vec3d &viewpoint)
	Compute the normals of an arbitrary point cloud computeNormalsPC3d uses a plane fitting approach to smoothly compute local normals. Normals are obtained through the eigenvector of the covariance matrix, corresponding to the smallest eigen value. If PCNormals is provided to be an Nx6 matrix, then no new allocation is made, instead the existing memory is overwritten. More...
void	destroyFlann (void *flannIndex)
void	getRandomPose (double Pose[16])
hashtable_int *	hashtable_int_clone (hashtable_int *hashtbl)
hashtable_int *	hashtableCreate (size_t size, size_t(*hashfunc)(unsigned int))
void	hashtableDestroy (hashtable_int *hashtbl)
void *	hashtableGet (hashtable_int *hashtbl, KeyType key)
hashnode_i *	hashtableGetBucketHashed (hashtable_int *hashtbl, KeyType key)
int	hashtableInsert (hashtable_int *hashtbl, KeyType key, void *data)
int	hashtableInsertHashed (hashtable_int *hashtbl, KeyType key, void *data)
void	hashtablePrint (hashtable_int *hashtbl)
hashtable_int *	hashtableRead (FILE *f)
int	hashtableRemove (hashtable_int *hashtbl, KeyType key)
int	hashtableResize (hashtable_int *hashtbl, size_t size)
int	hashtableWrite (const hashtable_int *hashtbl, const size_t dataSize, FILE *f)
void *	indexPCFlann (Mat pc)
Mat	loadPLYSimple (const char *fileName, int withNormals=0)
	Load a PLY file. More...
static unsigned int	next_power_of_two (unsigned int value)
	Round up to the next highest power of 2. More...
Mat	normalize_pc (Mat pc, float scale)
Mat	normalizePCCoeff (Mat pc, float scale, float *Cx, float *Cy, float *Cz, float *MinVal, float *MaxVal)
void	queryPCFlann (void *flannIndex, Mat &pc, Mat &indices, Mat &distances)
void	queryPCFlann (void *flannIndex, Mat &pc, Mat &indices, Mat &distances, const int numNeighbors)
Mat	samplePCByQuantization (Mat pc, float xrange[2], float yrange[2], float zrange[2], float sample_step_relative, int weightByCenter=0)
Mat	samplePCUniform (Mat PC, int sampleStep)
Mat	samplePCUniformInd (Mat PC, int sampleStep, std::vector< int > &indices)
Mat	transformPCPose (Mat pc, const double Pose[16])
Mat	transPCCoeff (Mat pc, float scale, float Cx, float Cy, float Cz, float MinVal, float MaxVal)
void	writePLY (Mat PC, const char *fileName)
	Write a point cloud to PLY file. More...
void	writePLYVisibleNormals (Mat PC, const char *fileName)
	Used for debbuging pruposes, writes a point cloud to a PLY file with the tip of the normal vectors as visible red points. More...