A generic kernel used for the ACRANSAC / LORANSAC framework. More...

#include <IRansacKernel.hpp>

Public Member Functions
virtual std::size_t	getMinimumNbRequiredSamples () const =0
	Return the minimum number of required samples for the solver. More...

virtual std::size_t	getMinimumNbRequiredSamplesLS () const =0
	Return the minimum number of required samples for the solver Ls. More...

virtual std::size_t	getMaximumNbModels () const =0
	Return the maximum number of models for the solver. More...

virtual void	fit (const std::vector< std::size_t > &samples, std::vector< ModelT > &models) const =0
	This function is called to estimate the model from the minimum number of sample `minSample` (i.e. minimal problem solver). More...

virtual void	fitLS (const std::vector< std::size_t > &inliers, std::vector< ModelT > &models, const std::vector< double > *weights=nullptr) const =0
	This function is called to estimate the model using a least squared algorithm from a minimum of `minSampleLS`. More...

virtual void	computeWeights (const ModelT &model, const std::vector< std::size_t > &inliers, std::vector< double > &weights, const double eps=0.001) const =0
	Function used to estimate the weights, typically used by the least square algorithm. More...

virtual double	error (std::size_t sample, const ModelT &model) const =0
	Function that computes the estimation error for a given model and a given element. More...

virtual void	errors (const ModelT &model, std::vector< double > &errors) const =0
	Function that computes the estimation error for a given model and all the elements. More...

virtual void	unnormalize (ModelT &model) const =0
	Function used to unnormalize the model. More...

virtual std::size_t	nbSamples () const =0
	The number of elements in the data. More...

virtual double	logalpha0 () const =0
	Get logalpha0, Alpha0 is used to make the error adaptive to the image size. More...

virtual double	errorVectorDimension () const =0

virtual double	unormalizeError (double val) const =0

virtual Mat3	normalizer1 () const =0

virtual double	thresholdNormalizer () const =0

Detailed Description

template<typename ModelT>
class aliceVision::robustEstimation::IRansacKernel< ModelT >

A generic kernel used for the ACRANSAC / LORANSAC framework.

Template Parameters

ModelT The class representing the model to estimate.

Note: Handle data normalization and compute the corresponding logalpha 0 that depends of the error model (point to line, or point to point)

Member Function Documentation

◆ computeWeights()

template<typename ModelT >

virtual void aliceVision::robustEstimation::IRansacKernel< ModelT >::computeWeights	(	const ModelT &	model,
		const std::vector< std::size_t > &	inliers,
		std::vector< double > &	weights,
		const double	eps = `0.001`
	)		const

pure virtual

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).

Implemented in aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, ModelT_, SolverLsT_ >, and aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >.

◆ error()

template<typename ModelT >

virtual double aliceVision::robustEstimation::IRansacKernel< ModelT >::error	(	std::size_t	sample,
		const ModelT &	model
	)		const

pure virtual

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

Parameters

[in]	sample	The index of the element for which the error is computed.
[in]	model	The model to consider.

Returns: The estimation error for the given element and the given model.

Implemented in aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, ModelT_, SolverLsT_ >, and aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >.

◆ errors()

template<typename ModelT >

virtual void aliceVision::robustEstimation::IRansacKernel< ModelT >::errors	(	const ModelT &	model,
		std::vector< double > &	errors
	)		const

pure virtual

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.

Implemented in aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, ModelT_, SolverLsT_ >, and aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >.

◆ fit()

template<typename ModelT >

virtual void aliceVision::robustEstimation::IRansacKernel< ModelT >::fit	(	const std::vector< std::size_t > &	samples,
		std::vector< ModelT > &	models
	)		const

pure virtual

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.

Implemented in aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >, and aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, ModelT_, SolverLsT_ >.

◆ fitLS()

template<typename ModelT >

virtual void aliceVision::robustEstimation::IRansacKernel< ModelT >::fitLS	(	const std::vector< std::size_t > &	inliers,
		std::vector< ModelT > &	models,
		const std::vector< double > *	weights = `nullptr`
	)		const

pure virtual

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

Implemented in aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, ModelT_, SolverLsT_ >, and aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >.

◆ getMaximumNbModels()

template<typename ModelT >

virtual std::size_t aliceVision::robustEstimation::IRansacKernel< ModelT >::getMaximumNbModels ( ) const

pure virtual

Return the maximum number of models for the solver.

Returns: maximum number of models

Implemented in aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, and aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >.

◆ getMinimumNbRequiredSamples()

template<typename ModelT >

virtual std::size_t aliceVision::robustEstimation::IRansacKernel< ModelT >::getMinimumNbRequiredSamples ( ) const

pure virtual

Return the minimum number of required samples for the solver.

Returns: minimum number of required samples

Implemented in aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, and aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >.

◆ getMinimumNbRequiredSamplesLS()

template<typename ModelT >

virtual std::size_t aliceVision::robustEstimation::IRansacKernel< ModelT >::getMinimumNbRequiredSamplesLS ( ) const

pure virtual

Return the minimum number of required samples for the solver Ls.

Returns: minimum number of required samples

◆ logalpha0()

template<typename ModelT >

virtual double aliceVision::robustEstimation::IRansacKernel< ModelT >::logalpha0 ( ) const

pure virtual

Get logalpha0, Alpha0 is used to make the error adaptive to the image size.

Returns: logalpha0

Implemented in aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, and aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >.

◆ nbSamples()

template<typename ModelT >

virtual std::size_t aliceVision::robustEstimation::IRansacKernel< ModelT >::nbSamples ( ) const

pure virtual

The number of elements in the data.

Returns: the number of elements in the data.

Implemented in aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, and aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >.

◆ unnormalize()

template<typename ModelT >

virtual void aliceVision::robustEstimation::IRansacKernel< ModelT >::unnormalize ( ModelT & model ) const

pure virtual

Function used to unnormalize the model.

Parameters

[in,out] model The model to unnormalize.

Implemented in aliceVision::multiview::TriangulationSphericalKernel, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat3Model, robustEstimation::UndefinedSolver< robustEstimation::Mat3Model > >, aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, robustEstimation::Mat34Model, robustEstimation::UndefinedSolver< robustEstimation::Mat34Model > >, aliceVision::sfm::TranslationTripletKernelACRansac< SolverT_, ErrorT_, ModelT_ >, and aliceVision::robustEstimation::PointFittingRansacKernel< SolverT_, ErrorT_, ModelT_, SolverLsT_ >.

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

/home/docs/checkouts/readthedocs.org/user_builds/alicevision/checkouts/latest/src/aliceVision/robustEstimation/IRansacKernel.hpp

Public Member Functions

Detailed Description

template<typename ModelT> class aliceVision::robustEstimation::IRansacKernel< ModelT >

Member Function Documentation

◆ computeWeights()

◆ error()

◆ errors()

◆ fit()

◆ fitLS()

◆ getMaximumNbModels()

◆ getMinimumNbRequiredSamples()

◆ getMinimumNbRequiredSamplesLS()

◆ logalpha0()

◆ nbSamples()

◆ unnormalize()

template<typename ModelT>
class aliceVision::robustEstimation::IRansacKernel< ModelT >