AContrario Kernel to solve a translation triplet & structure problem. More...

#include <TranslationTripletKernelACRansac.hpp>

Inheritance diagram for aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >:

Public Types
using	SolverT = SolverT_

using	ErrorT = SolverT_

using	ModelT = ModelT_

Public Member Functions
	TranslationTripletKernelACRansac (const Mat &x1, const Mat &x2, const Mat &x3, const std::vector< Mat3 > &vec_KRi, const Mat3 &K, const double ThresholdUpperBound)

std::size_t	getMinimumNbRequiredSamples () const override
	Return the minimum number of required samples for the solver. More...

std::size_t	getMaximumNbModels () const override
	Return the maximum number of models for the solver. More...

void	fit (const std::vector< std::size_t > &samples, std::vector< ModelT_ > &models) const override
	This function is called to estimate the model from the minimum number of sample `minSample` (i.e. minimal problem solver). More...

double	error (std::size_t sample, const ModelT_ &model) const override
	error More...

void	errors (const ModelT_ &model, std::vector< double > &errors) const override
	Function that computes the estimation error for a given model and all the elements. More...

std::size_t	nbSamples () const override

void	unnormalize (ModelT_ &model) const override
	Function used to unnormalize the model. More...

double	logalpha0 () const override

double	errorVectorDimension () const override

Mat3	normalizer1 () const override

double	thresholdNormalizer () const override

double	unormalizeError (double val) const override

std::size_t	getMinimumNbRequiredSamplesLS () const

void	fitLS (const std::vector< std::size_t > &inliers, std::vector< ModelT > &models, const std::vector< double > *weights=nullptr) const
	This function is called to estimate the model using a least squared algorithm from a minimum of `minSampleLS`. More...

void	computeWeights (const ModelT &model, const std::vector< std::size_t > &inliers, std::vector< double > &weights, const double eps=0.001) const
	Function used to estimate the weights, typically used by the least square algorithm. More...

Detailed Description

template<typename SolverT_, typename ErrorT_, typename ModelT_>
class aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >

AContrario Kernel to solve a translation triplet & structure problem.

Member Function Documentation

◆ computeWeights()

template<typename SolverT_ , typename ErrorT_ , typename ModelT_ >

void aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >::computeWeights	(	const ModelT &	model,
		const std::vector< std::size_t > &	inliers,
		std::vector< double > &	weights,
		const double	eps = `0.001`
	)		const

inlinevirtual

Function used to estimate the weights, typically used by the least square algorithm.

Parameters

[in]	model	The model against which the weights are computed.
[in]	inliers	The array of the indices of the data to be used.
[out]	vec_weights	The array of weight of the same size as `inliers`.
[in]	eps	An optional threshold to max out the value of the threshold (typically to avoid division by zero or too small numbers).

Implements aliceVision::robustEstimation::IRansacKernel< ModelT_ >.

◆ error()

template<typename SolverT_ , typename ErrorT_ , typename ModelT_ >

double aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >::error	(	std::size_t	sample,
		const ModelT_ &	model
	)		const

inlineoverridevirtual

error

Parameters

sample
model

Returns

Implements aliceVision::robustEstimation::IRansacKernel< ModelT_ >.

◆ errors()

template<typename SolverT_ , typename ErrorT_ , typename ModelT_ >

void aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >::errors	(	const ModelT_ &	model,
		std::vector< double > &	errors
	)		const

inlineoverridevirtual

Function that computes the estimation error for a given model and all the elements.

Parameters

[in]	model	The model to consider.
[out]	vec_errors	The vector containing all the estimation errors for every element.

Implements aliceVision::robustEstimation::IRansacKernel< ModelT_ >.

◆ fit()

template<typename SolverT_ , typename ErrorT_ , typename ModelT_ >

void aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >::fit	(	const std::vector< std::size_t > &	samples,
		std::vector< ModelT_ > &	models
	)		const

inlineoverridevirtual

This function is called to estimate the model from the minimum number of sample minSample (i.e. minimal problem solver).

Parameters

[in]	samples	A vector containing the indices of the data to be used for the minimal estimation.
[out]	models	The model(s) estimated by the minimal solver.

Implements aliceVision::robustEstimation::IRansacKernel< ModelT_ >.

◆ fitLS()

template<typename SolverT_ , typename ErrorT_ , typename ModelT_ >

void aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >::fitLS	(	const std::vector< std::size_t > &	inliers,
		std::vector< ModelT > &	models,
		const std::vector< double > *	weights = `nullptr`
	)		const

inlinevirtual

This function is called to estimate the model using a least squared algorithm from a minimum of minSampleLS.

Parameters

[in]	inliers	An array containing the indices of the data to use.
[out]	models	The model(s) estimated using the least squared algorithm.
[in]	weights	An optional array of weights, one for each sample

Implements aliceVision::robustEstimation::IRansacKernel< ModelT_ >.

◆ getMaximumNbModels()

template<typename SolverT_ , typename ErrorT_ , typename ModelT_ >

std::size_t aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >::getMaximumNbModels ( ) const

inlineoverride

Return the maximum number of models for the solver.

Returns: maximum number of models

◆ getMinimumNbRequiredSamples()

template<typename SolverT_ , typename ErrorT_ , typename ModelT_ >

std::size_t aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >::getMinimumNbRequiredSamples ( ) const

inlineoverride

Return the minimum number of required samples for the solver.

Returns: minimum number of required samples

◆ unnormalize()

template<typename SolverT_ , typename ErrorT_ , typename ModelT_ >

void aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >::unnormalize ( ModelT_ & model ) const

inlineoverridevirtual

Function used to unnormalize the model.

Parameters

[in,out] model The model to unnormalize.

Implements aliceVision::robustEstimation::IRansacKernel< ModelT_ >.

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

/home/docs/checkouts/readthedocs.org/user_builds/alicevision/checkouts/latest/src/aliceVision/sfm/pipeline/global/TranslationTripletKernelACRansac.hpp

Public Types

Public Member Functions

Detailed Description

template<typename SolverT_, typename ErrorT_, typename ModelT_> class aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >

Member Function Documentation

◆ computeWeights()

◆ error()

◆ errors()

◆ fit()

◆ fitLS()

◆ getMaximumNbModels()

◆ getMinimumNbRequiredSamples()

◆ unnormalize()

template<typename SolverT_, typename ErrorT_, typename ModelT_>
class aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >