class for planar RANSAC estimation on poincloud data (xyzh). More...

#include <PlanarRansacEstimator.h>

Classes
struct	Result

Public Types
enum	Mode { BEST, GPU, CPU }

enum	OptimizationCriterion { MAX_ON =1, ON_ONE_SIDE =2 }

Public Member Functions
	PlanarRansacEstimator (Mode mode=BEST)
	Constructor. More...

	~PlanarRansacEstimator ()
	Destructor. More...

Result	apply (core::DataSegment< float, 4 > &xyzh, std::vector< int > &srcIDs, std::vector< int > &dstIDs, float threshold, int passes, int subset, int tolerance, int optimization)
	Applies the planar RANSAC estimation on a destination region with a model from the src region (e.g. for best fitting plane src=dst). More...

Result	apply (std::vector< Vec > &srcPoints, std::vector< Vec > &dstPoints, float threshold, int passes, int subset, int tolerance, int optimization)
	Applies the planar RANSAC estimation on a destination region with a model from the src region (e.g. for best fitting plane src=dst). More...

math::DynMatrix< Result >	apply (core::DataSegment< float, 4 > &xyzh, std::vector< std::vector< int > > &pointIDs, math::DynMatrix< bool > &testMatrix, float threshold, int passes, int tolerance, int optimization, core::Img32s labelImage)
	Applies the planar RANSAC estimation on multiple pairs of surfaces (given by a boolean test matrix). More...

void	relabel (core::DataSegment< float, 4 > &xyzh, core::Img8u &newMask, core::Img32s &oldLabel, core::Img32s &newLabel, int desiredID, int srcID, float threshold, Result &result)
	Create a label image of all points on the planar model (incl. the original surface) More...

Static Public Member Functions
static void	calculateRandomModels (std::vector< Vec > &srcPoints, std::vector< Vec > &n0, std::vector< float > &dist, int passes)
	Creates random models (n and distance) for RANSAC. More...

static void	calculateRandomModels (core::DataSegment< float, 4 > &xyzh, std::vector< int > &srcPoints, std::vector< Vec > &n0, std::vector< float > &dist, int passes)
	Creates random models (n and distance) for RANSAC. More...

Private Member Functions
void	calculateMultiCL (core::DataSegment< float, 4 > &xyzh, core::Img32s labelImage, math::DynMatrix< bool > &testMatrix, float threshold, int passes, std::vector< Vec > &n0, std::vector< float > &dist, std::vector< int > &cAbove, std::vector< int > &cBelow, std::vector< int > &cOn, std::vector< int > &adjs, std::vector< int > &start, std::vector< int > &end)

void	calculateMultiCPU (core::DataSegment< float, 4 > &xyzh, std::vector< std::vector< int > > &pointIDs, math::DynMatrix< bool > &testMatrix, float threshold, int passes, std::vector< std::vector< Vec > > &n0Pre, std::vector< std::vector< float > > &distPre, std::vector< int > &cAbove, std::vector< int > &cBelow, std::vector< int > &cOn, std::vector< int > &adjs, std::vector< int > &start, std::vector< int > &end)

void	calculateSingleCL (std::vector< Vec > &dstPoints, float threshold, int passes, int subset, std::vector< Vec > &n0, std::vector< float > &dist, std::vector< int > &cAbove, std::vector< int > &cBelow, std::vector< int > &cOn)

void	calculateSingleCPU (std::vector< Vec > &dstPoints, float threshold, int passes, int subset, std::vector< Vec > &n0, std::vector< float > &dist, std::vector< int > &cAbove, std::vector< int > &cBelow, std::vector< int > &cOn)

void	initOpenCL ()

Result	createResult (std::vector< Vec > &n0, std::vector< float > &dist, std::vector< int > &cAbove, std::vector< int > &cBelow, std::vector< int > &cOn, float threshold, int passes, int tolerance, int optimization, int numPoints)

math::DynMatrix< Result >	createResultMatrix (math::DynMatrix< bool > &testMatrix, std::vector< int > &start, std::vector< int > &end, std::vector< int > &adjs, std::vector< int > &cAbove, std::vector< int > &cBelow, std::vector< int > &cOn, std::vector< std::vector< int > > &pointIDs, std::vector< std::vector< Vec > > &n0Pre, std::vector< std::vector< float > > &distPre, float threshold, int passes, int tolerance, int optimization)

void	relabelCL (core::DataSegment< float, 4 > &xyzh, core::Img8u &newMask, core::Img32s &oldLabel, core::Img32s &newLabel, int desiredID, int srcID, float threshold, Result &result, int w, int h)

void	relabelCPU (core::DataSegment< float, 4 > &xyzh, core::Img8u &newMask, core::Img32s &oldLabel, core::Img32s &newLabel, int desiredID, int srcID, float threshold, Result &result, int w, int h)

Static Private Member Functions
static void	calculateModel (Vec &fa, Vec &fb, Vec &rPoint, Vec &n0, float &dist)

Private Attributes
Data *	m_data
	internal data type More...

Detailed Description

class for planar RANSAC estimation on poincloud data (xyzh).

The PlanarRansacEstimator class does not minimize the error but simply counts the points with a distance smaller a given threshold. The smaller the threshold the preciser the model. Additionally, it is possible to assign all points on the plane to the initial surface.

Member Enumeration Documentation

◆ Mode

enum icl::geom::PlanarRansacEstimator::Mode

Enumerator
BEST
GPU
CPU

◆ OptimizationCriterion

enum icl::geom::PlanarRansacEstimator::OptimizationCriterion

Enumerator
MAX_ON
ON_ONE_SIDE

Constructor & Destructor Documentation

◆ PlanarRansacEstimator()

icl::geom::PlanarRansacEstimator::PlanarRansacEstimator ( Mode mode = BEST )

Constructor.

Constructs an object of this class.

Parameters

mode	the selected mode: CPU, GPU or BEST (uses GPU if available)

◆ ~PlanarRansacEstimator()

icl::geom::PlanarRansacEstimator::~PlanarRansacEstimator ( )

Destructor.

Member Function Documentation

◆ apply() [1/3]

Result icl::geom::PlanarRansacEstimator::apply	(	core::DataSegment< float, 4 > &	xyzh,
		std::vector< int > &	srcIDs,
		std::vector< int > &	dstIDs,
		float	threshold,
		int	passes,
		int	subset,
		int	tolerance,
		int	optimization
	)

Applies the planar RANSAC estimation on a destination region with a model from the src region (e.g. for best fitting plane src=dst).

Parameters

xyzh	the input xyzh from the pointcloud
srcIDs	vector of IDs (pointcloud position – for structured x+y*w) of the source surface (for model determination)
dstIDs	vector of IDs of the destination surface (for matching)
threshold	the maximal euclidean distance in mm
passes	number of ransac passes
subset	the subset of points for matching (2 means every second point)
tolerance	number of points allowed not to be on one single side of the object for ON_ONE_SIDE
optimization	the optimization criterion (ON_ONE_SIDE e.g. for cutfree adjacency test or MAX_ON e.g. for best fitting plane)

Returns: the Result struct.

◆ apply() [2/3]

Result icl::geom::PlanarRansacEstimator::apply	(	std::vector< Vec > &	srcPoints,
		std::vector< Vec > &	dstPoints,
		float	threshold,
		int	passes,
		int	subset,
		int	tolerance,
		int	optimization
	)

Applies the planar RANSAC estimation on a destination region with a model from the src region (e.g. for best fitting plane src=dst).

Parameters

xyzh	the input xyzh from the pointcloud
srcPoints	vector of pointcloud points of the source surface (for model determination)
dstIDs	vector of pointcloud points of the destination surface (for matching)
threshold	the maximal euclidean distance in mm
passes	number of ransac passes
subset	the subset of points for matching (2 means every second point)
tolerance	number of points allowed not to be on one single side of the object for ON_ONE_SIDE
optimization	the optimization criterion (ON_ONE_SIDE e.g. for cutfree adjacency test or MAX_ON e.g. for best fitting plane)

Returns: the Result struct.

◆ apply() [3/3]

math::DynMatrix<Result> icl::geom::PlanarRansacEstimator::apply	(	core::DataSegment< float, 4 > &	xyzh,
		std::vector< std::vector< int > > &	pointIDs,
		math::DynMatrix< bool > &	testMatrix,
		float	threshold,
		int	passes,
		int	tolerance,
		int	optimization,
		core::Img32s	labelImage
	)

Applies the planar RANSAC estimation on multiple pairs of surfaces (given by a boolean test matrix).

Parameters

xyzh	the input xyzh from the pointcloud
pointsIDs	vector (surfaces) of vector IDs (pointcloud position – for structured x+y*w) of the surfaces
testMatrix	boolean matrix for surface pair testing (1 = test, 0 = dont test)
threshold	the maximal euclidean distance in mm
passes	number of ransac passes
tolerance	number of points allowed not to be on one single side of the object for ON_ONE_SIDE
optimization	the optimization criterion (ON_ONE_SIDE e.g. for cutfree adjacency test or MAX_ON e.g. for best fitting plane)
labelImage	the labelImage with the point label

Returns: matrix of Result structs.

◆ calculateModel()

static void icl::geom::PlanarRansacEstimator::calculateModel	(	Vec &	fa,
		Vec &	fb,
		Vec &	rPoint,
		Vec &	n0,
		float &	dist
	)

staticprivate

◆ calculateMultiCL()

void icl::geom::PlanarRansacEstimator::calculateMultiCL	(	core::DataSegment< float, 4 > &	xyzh,
		core::Img32s	labelImage,
		math::DynMatrix< bool > &	testMatrix,
		float	threshold,
		int	passes,
		std::vector< Vec > &	n0,
		std::vector< float > &	dist,
		std::vector< int > &	cAbove,
		std::vector< int > &	cBelow,
		std::vector< int > &	cOn,
		std::vector< int > &	adjs,
		std::vector< int > &	start,
		std::vector< int > &	end
	)

private

◆ calculateMultiCPU()

void icl::geom::PlanarRansacEstimator::calculateMultiCPU	(	core::DataSegment< float, 4 > &	xyzh,
		std::vector< std::vector< int > > &	pointIDs,
		math::DynMatrix< bool > &	testMatrix,
		float	threshold,
		int	passes,
		std::vector< std::vector< Vec > > &	n0Pre,
		std::vector< std::vector< float > > &	distPre,
		std::vector< int > &	cAbove,
		std::vector< int > &	cBelow,
		std::vector< int > &	cOn,
		std::vector< int > &	adjs,
		std::vector< int > &	start,
		std::vector< int > &	end
	)

private

◆ calculateRandomModels() [1/2]

static void icl::geom::PlanarRansacEstimator::calculateRandomModels	(	std::vector< Vec > &	srcPoints,
		std::vector< Vec > &	n0,
		std::vector< float > &	dist,
		int	passes
	)

static

Creates random models (n and distance) for RANSAC.

Parameters

srcPoints	the input points
n0	the empty input n0 vector
dist	the empty input distance vector
passes	the number of passes

◆ calculateRandomModels() [2/2]

static void icl::geom::PlanarRansacEstimator::calculateRandomModels	(	core::DataSegment< float, 4 > &	xyzh,
		std::vector< int > &	srcPoints,
		std::vector< Vec > &	n0,
		std::vector< float > &	dist,
		int	passes
	)

static

Creates random models (n and distance) for RANSAC.

Parameters

xyzh	the input xyz pointcloud data
srcPoints	the sourcePoint Ids
n0	the empty input n0 vector
dist	the empty input distance vector
passes	the number of passes

◆ calculateSingleCL()

void icl::geom::PlanarRansacEstimator::calculateSingleCL	(	std::vector< Vec > &	dstPoints,
		float	threshold,
		int	passes,
		int	subset,
		std::vector< Vec > &	n0,
		std::vector< float > &	dist,
		std::vector< int > &	cAbove,
		std::vector< int > &	cBelow,
		std::vector< int > &	cOn
	)

private

◆ calculateSingleCPU()

void icl::geom::PlanarRansacEstimator::calculateSingleCPU	(	std::vector< Vec > &	dstPoints,
		float	threshold,
		int	passes,
		int	subset,
		std::vector< Vec > &	n0,
		std::vector< float > &	dist,
		std::vector< int > &	cAbove,
		std::vector< int > &	cBelow,
		std::vector< int > &	cOn
	)

private

◆ createResult()

Result icl::geom::PlanarRansacEstimator::createResult	(	std::vector< Vec > &	n0,
		std::vector< float > &	dist,
		std::vector< int > &	cAbove,
		std::vector< int > &	cBelow,
		std::vector< int > &	cOn,
		float	threshold,
		int	passes,
		int	tolerance,
		int	optimization,
		int	numPoints
	)

private

◆ createResultMatrix()

math::DynMatrix<Result> icl::geom::PlanarRansacEstimator::createResultMatrix	(	math::DynMatrix< bool > &	testMatrix,
		std::vector< int > &	start,
		std::vector< int > &	end,
		std::vector< int > &	adjs,
		std::vector< int > &	cAbove,
		std::vector< int > &	cBelow,
		std::vector< int > &	cOn,
		std::vector< std::vector< int > > &	pointIDs,
		std::vector< std::vector< Vec > > &	n0Pre,
		std::vector< std::vector< float > > &	distPre,
		float	threshold,
		int	passes,
		int	tolerance,
		int	optimization
	)

private

◆ initOpenCL()

void icl::geom::PlanarRansacEstimator::initOpenCL ( )

private

◆ relabel()

void icl::geom::PlanarRansacEstimator::relabel	(	core::DataSegment< float, 4 > &	xyzh,
		core::Img8u &	newMask,
		core::Img32s &	oldLabel,
		core::Img32s &	newLabel,
		int	desiredID,
		int	srcID,
		float	threshold,
		Result &	result
	)

Create a label image of all points on the planar model (incl. the original surface)

Parameters

xyzh	the input xyzh from the pointcloud
newMask	the mask (e.g. ROI) for the relabeling
oldLabel	the original label image
newLabel	the resulting label image with all points on the model
desiredID	the id in the resulting label image
srcID	the ID of the label image from the fitted surface
threshold	the maximum euclidean distance for relabeling
result	the result struct returned by apply (contains the model and the original surface ID)

◆ relabelCL()

void icl::geom::PlanarRansacEstimator::relabelCL	(	core::DataSegment< float, 4 > &	xyzh,
		core::Img8u &	newMask,
		core::Img32s &	oldLabel,
		core::Img32s &	newLabel,
		int	desiredID,
		int	srcID,
		float	threshold,
		Result &	result,
		int	w,
		int	h
	)

private

◆ relabelCPU()

void icl::geom::PlanarRansacEstimator::relabelCPU	(	core::DataSegment< float, 4 > &	xyzh,
		core::Img8u &	newMask,
		core::Img32s &	oldLabel,
		core::Img32s &	newLabel,
		int	desiredID,
		int	srcID,
		float	threshold,
		Result &	result,
		int	w,
		int	h
	)

private

Member Data Documentation

◆ m_data

Data* icl::geom::PlanarRansacEstimator::m_data

private

internal data type

internal data pointer

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

/Users/alneuman/vm/icl/ICLGeom/src/ICLGeom/PlanarRansacEstimator.h

Classes

Public Types

Public Member Functions

Static Public Member Functions

Private Member Functions

Static Private Member Functions

Private Attributes

Detailed Description

Member Enumeration Documentation

◆ Mode

◆ OptimizationCriterion

Constructor & Destructor Documentation

◆ PlanarRansacEstimator()

◆ ~PlanarRansacEstimator()

Member Function Documentation

◆ apply() [1/3]

◆ apply() [2/3]

◆ apply() [3/3]

◆ calculateModel()

◆ calculateMultiCL()

◆ calculateMultiCPU()

◆ calculateRandomModels() [1/2]

◆ calculateRandomModels() [2/2]

◆ calculateSingleCL()

◆ calculateSingleCPU()

◆ createResult()

◆ createResultMatrix()

◆ initOpenCL()

◆ relabel()

◆ relabelCL()

◆ relabelCPU()

Member Data Documentation

◆ m_data