#include <TriangulationSphericalKernel.hpp>

Inheritance diagram for aliceVision::multiview::TriangulationSphericalKernel:

Public Types
using	ModelT = robustEstimation::MatrixModel< Vec4 >

Public Member Functions
	TriangulationSphericalKernel (const std::vector< Vec2 > &observations, const std::vector< double > &weights, const std::vector< Eigen::Matrix4d > &poses, std::vector< std::shared_ptr< camera::IntrinsicBase >> &intrinsics)
	Constructor. More...

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

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

std::size_t	getMinimumNbRequiredSamplesLS () const override

void	fit (const std::vector< std::size_t > &samples, std::vector< ModelT > &models) const override
	Triangulate 2 points. More...

void	fitLS (const std::vector< std::size_t > &inliers, std::vector< ModelT > &models, const std::vector< double > *weights=nullptr) const override
	Triangulate N points using the least squared solver. More...

void	computeWeights (const ModelT &model, const std::vector< std::size_t > &inliers, std::vector< double > &weights, const double eps=0.001) const override
	Compute the weights.. More...

double	error (std::size_t sample, const ModelT &model) const override
	Error for the i-th observation. More...

void	errors (const ModelT &model, std::vector< double > &errors) const override
	Error for each view. More...

void	unnormalize (robustEstimation::MatrixModel< Vec4 > &model) const override
	Unnormalize the model. (not used) More...

std::size_t	nbSamples () const override
	Return the number of view. More...

double	logalpha0 () const override

double	errorVectorDimension () const override

double	unormalizeError (double val) const override

Mat3	normalizer1 () const override

double	thresholdNormalizer () const override

Public Member Functions inherited from aliceVision::robustEstimation::IRansacKernel< robustEstimation::MatrixModel< Vec4 > >
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< robustEstimation::MatrixModel< Vec4 > > &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< robustEstimation::MatrixModel< Vec4 > > &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 robustEstimation::MatrixModel< Vec4 > &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 robustEstimation::MatrixModel< Vec4 > &model) const=0
	Function that computes the estimation error for a given model and a given element. More...

virtual void	errors (const robustEstimation::MatrixModel< Vec4 > &model, std::vector< double > &errors) const=0
	Function that computes the estimation error for a given model and all the elements. 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 Mat3	normalizer1 () const=0

virtual double	thresholdNormalizer () const=0

Detailed Description

Triangulation kernel which works for any camera model. The estimation is done on lifted points but error is computed in pixels

Constructor & Destructor Documentation

◆ TriangulationSphericalKernel()

aliceVision::multiview::TriangulationSphericalKernel::TriangulationSphericalKernel	(	const std::vector< Vec2 > &	observations,
		const std::vector< double > &	weights,
		const std::vector< Eigen::Matrix4d > &	poses,
		std::vector< std::shared_ptr< camera::IntrinsicBase >> &	intrinsics
	)

inline

Constructor.

Parameters

[in]	observations	The observations 2d points in pixels.
[in]	weights	The observations weights.
[in]	poses	the transformation for each observation.
[in]	intrinsics	the camera intrinsic for each observation.

Member Function Documentation

◆ computeWeights()

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

inlineoverride

Compute the weights..

Parameters

[in]	model	The 3D point for 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).

◆ error()

double aliceVision::multiview::TriangulationSphericalKernel::error	(	std::size_t	sample,
		const ModelT &	model
	)		const

inlineoverride

Error for the i-th observation.

Parameters

[in]	sample	The index of the observation for which the error is computed.
[in]	model	The 3D point.

Returns: The estimation error for the given observation and 3D point.

◆ errors()

void aliceVision::multiview::TriangulationSphericalKernel::errors	(	const ModelT &	model,
		std::vector< double > &	errors
	)		const

inlineoverride

Error for each view.

Parameters

[in]	model	The 3D point.
[out]	vec_errors	The vector containing all the estimation errors for every view.

◆ fit()

void aliceVision::multiview::TriangulationSphericalKernel::fit	(	const std::vector< std::size_t > &	samples,
		std::vector< ModelT > &	models
	)		const

inlineoverride

Triangulate 2 points.

Parameters

[in]	samples	The index of two points to triangulate.
[out]	models	The estimated 3D points.

◆ fitLS()

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

inlineoverride

Triangulate N points using the least squared solver.

Parameters

[in]	inliers	The index of the N points to triangulate.
[out]	models	The estimated 3D point.
[in]	weights	The optional array of weight for each of the N points.

◆ getMaximumNbModels()

std::size_t aliceVision::multiview::TriangulationSphericalKernel::getMaximumNbModels ( ) const

inlineoverride

Return the maximum number of models.

Returns: maximum number of models

◆ getMinimumNbRequiredSamples()

std::size_t aliceVision::multiview::TriangulationSphericalKernel::getMinimumNbRequiredSamples ( ) const

inlineoverride

Return the minimum number of required samples.

Returns: minimum number of required samples

◆ nbSamples()

std::size_t aliceVision::multiview::TriangulationSphericalKernel::nbSamples ( ) const

inlineoverride

Return the number of view.

Returns: the number of view.

◆ unnormalize()

void aliceVision::multiview::TriangulationSphericalKernel::unnormalize ( robustEstimation::MatrixModel< Vec4 > & model ) const

inlineoverridevirtual

Unnormalize the model. (not used)

Parameters

[in,out] model the 3D point.

Implements aliceVision::robustEstimation::IRansacKernel< robustEstimation::MatrixModel< Vec4 > >.

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

/home/docs/checkouts/readthedocs.org/user_builds/alicevision/checkouts/latest/src/aliceVision/multiview/triangulation/TriangulationSphericalKernel.hpp

Public Types

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ TriangulationSphericalKernel()

Member Function Documentation

◆ computeWeights()

◆ error()

◆ errors()

◆ fit()

◆ fitLS()

◆ getMaximumNbModels()

◆ getMinimumNbRequiredSamples()

◆ nbSamples()

◆ unnormalize()