cv::ppf_match_3d::PPF3DDetector Class Reference

Class, allowing the load and matching 3D models. Typical Use: More...

#include <opencv2/surface_matching/ppf_match_3d.hpp>

Public Member Functions
	PPF3DDetector ()
	Empty constructor. Sets default arguments. More...
	PPF3DDetector (const double relativeSamplingStep, const double relativeDistanceStep=0.05, const double numAngles=30)
virtual	~PPF3DDetector ()
void	match (const Mat &scene, std::vector< Pose3DPtr > &results, const double relativeSceneSampleStep=1.0/5.0, const double relativeSceneDistance=0.03)
	Matches a trained model across a provided scene. More...
void	read (const FileNode &fn)
void	setSearchParams (const double positionThreshold=-1, const double rotationThreshold=-1, const bool useWeightedClustering=false)
void	trainModel (const Mat &Model)
	Trains a new model. More...
void	write (FileStorage &fs) const

Protected Member Functions
void	clearTrainingModels ()

Protected Attributes
double	angle_step
double	angle_step_radians
double	angle_step_relative
double	distance_step
double	distance_step_relative
THash *	hash_nodes
hashtable_int *	hash_table
int	num_ref_points
double	position_threshold
Mat	ppf
double	rotation_threshold
Mat	sampled_pc
double	sampling_step_relative
int	scene_sample_step
bool	use_weighted_avg

Detailed Description

Class, allowing the load and matching 3D models. Typical Use:

// Train a model
ppf_match_3d::PPF3DDetector detector(0.05, 0.05);
detector.trainModel(pc);
// Search the model in a given scene
vector<Pose3DPtr> results;
detector.match(pcTest, results, 1.0/5.0,0.05);

Constructor & Destructor Documentation

PPF3DDetector() [1/2]

cv::ppf_match_3d::PPF3DDetector::PPF3DDetector

(

)

Python:
	cv.ppf_match_3d.PPF3DDetector(		) ->	<ppf_match_3d_PPF3DDetector object>
	cv.ppf_match_3d.PPF3DDetector(	relativeSamplingStep[, relativeDistanceStep[, numAngles]]	) ->	<ppf_match_3d_PPF3DDetector object>

Empty constructor. Sets default arguments.

PPF3DDetector() [2/2]

cv::ppf_match_3d::PPF3DDetector::PPF3DDetector	(	const double	relativeSamplingStep,
		const double	relativeDistanceStep = 0.05,
		const double	numAngles = 30
	)

Python:
	cv.ppf_match_3d.PPF3DDetector(		) ->	<ppf_match_3d_PPF3DDetector object>
	cv.ppf_match_3d.PPF3DDetector(	relativeSamplingStep[, relativeDistanceStep[, numAngles]]	) ->	<ppf_match_3d_PPF3DDetector object>

Constructor with arguments

Parameters

[in]	relativeSamplingStep	Sampling distance relative to the object's diameter. Models are first sampled uniformly in order to improve efficiency. Decreasing this value leads to a denser model, and a more accurate pose estimation but the larger the model, the slower the training. Increasing the value leads to a less accurate pose computation but a smaller model and faster model generation and matching. Beware of the memory consumption when using small values.
[in]	relativeDistanceStep	The discretization distance of the point pair distance relative to the model's diameter. This value has a direct impact on the hashtable. Using small values would lead to too fine discretization, and thus ambiguity in the bins of hashtable. Too large values would lead to no discrimination over the feature vectors and different point pair features would be assigned to the same bin. This argument defaults to the value of RelativeSamplingStep. For noisy scenes, the value can be increased to improve the robustness of the matching against noisy points.
[in]	numAngles	Set the discretization of the point pair orientation as the number of subdivisions of the angle. This value is the equivalent of RelativeDistanceStep for the orientations. Increasing the value increases the precision of the matching but decreases the robustness against incorrect normal directions. Decreasing the value decreases the precision of the matching but increases the robustness against incorrect normal directions. For very noisy scenes where the normal directions can not be computed accurately, the value can be set to 25 or 20.

~PPF3DDetector()

virtual cv::ppf_match_3d::PPF3DDetector::~PPF3DDetector ( )

virtual

Member Function Documentation

clearTrainingModels()

void cv::ppf_match_3d::PPF3DDetector::clearTrainingModels ( )

protected

match()

void cv::ppf_match_3d::PPF3DDetector::match	(	const Mat &	scene,
		std::vector< Pose3DPtr > &	results,
		const double	relativeSceneSampleStep = 1.0/5.0,
		const double	relativeSceneDistance = 0.03
	)

Python:
	cv.ppf_match_3d.PPF3DDetector.match(	scene[, relativeSceneSampleStep[, relativeSceneDistance]]	) ->	results

Matches a trained model across a provided scene.

Parameters

[in]	scene	Point cloud for the scene
[out]	results	List of output poses
[in]	relativeSceneSampleStep	The ratio of scene points to be used for the matching after sampling with relativeSceneDistance. For example, if this value is set to 1.0/5.0, every 5th point from the scene is used for pose estimation. This parameter allows an easy trade-off between speed and accuracy of the matching. Increasing the value leads to less points being used and in turn to a faster but less accurate pose computation. Decreasing the value has the inverse effect.
[in]	relativeSceneDistance	Set the distance threshold relative to the diameter of the model. This parameter is equivalent to relativeSamplingStep in the training stage. This parameter acts like a prior sampling with the relativeSceneSampleStep parameter.

read()

void cv::ppf_match_3d::PPF3DDetector::read

(

const FileNode &

fn

)

setSearchParams()

void cv::ppf_match_3d::PPF3DDetector::setSearchParams	(	const double	positionThreshold = -1,
		const double	rotationThreshold = -1,
		const bool	useWeightedClustering = false
	)

Set the parameters for the search

Parameters

[in]	positionThreshold	Position threshold controlling the similarity of translations. Depends on the units of calibration/model.
[in]	rotationThreshold	Position threshold controlling the similarity of rotations. This parameter can be perceived as a threshold over the difference of angles
[in]	useWeightedClustering	The algorithm by default clusters the poses without weighting. A non-zero value would indicate that the pose clustering should take into account the number of votes as the weights and perform a weighted averaging instead of a simple one.

trainModel()

void cv::ppf_match_3d::PPF3DDetector::trainModel

(

const Mat &

Model

)

Python:
	cv.ppf_match_3d.PPF3DDetector.trainModel(	Model	) ->	None

Trains a new model.

Parameters

[in]

Model

The input point cloud with normals (Nx6)

Uses the parameters set in the constructor to downsample and learn a new model. When the model is learnt, the instance gets ready for calling "match".

write()

void cv::ppf_match_3d::PPF3DDetector::write

(

FileStorage &

fs

)

const

Member Data Documentation

angle_step

double cv::ppf_match_3d::PPF3DDetector::angle_step

protected

angle_step_radians

double cv::ppf_match_3d::PPF3DDetector::angle_step_radians

protected

angle_step_relative

double cv::ppf_match_3d::PPF3DDetector::angle_step_relative

protected

distance_step

double cv::ppf_match_3d::PPF3DDetector::distance_step

protected

distance_step_relative

double cv::ppf_match_3d::PPF3DDetector::distance_step_relative

protected

hash_nodes

THash* cv::ppf_match_3d::PPF3DDetector::hash_nodes

protected

hash_table

hashtable_int* cv::ppf_match_3d::PPF3DDetector::hash_table

protected

num_ref_points

int cv::ppf_match_3d::PPF3DDetector::num_ref_points

protected

position_threshold

double cv::ppf_match_3d::PPF3DDetector::position_threshold

protected

ppf

Mat cv::ppf_match_3d::PPF3DDetector::ppf

protected

rotation_threshold

double cv::ppf_match_3d::PPF3DDetector::rotation_threshold

protected

sampled_pc

Mat cv::ppf_match_3d::PPF3DDetector::sampled_pc

protected

sampling_step_relative

double cv::ppf_match_3d::PPF3DDetector::sampling_step_relative

protected

scene_sample_step

int cv::ppf_match_3d::PPF3DDetector::scene_sample_step

protected

use_weighted_avg

bool cv::ppf_match_3d::PPF3DDetector::use_weighted_avg

protected

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

• opencv2/surface_matching/ppf_match_3d.hpp