Reconstruction

Functions
void	cv::sfm::reconstruct (InputArrayOfArrays points2d, OutputArray Ps, OutputArray points3d, InputOutputArray K, bool is_projective=false)
	Reconstruct 3d points from 2d correspondences while performing autocalibration.
void	cv::sfm::reconstruct (InputArrayOfArrays points2d, OutputArray Rs, OutputArray Ts, InputOutputArray K, OutputArray points3d, bool is_projective=false)
	Reconstruct 3d points from 2d correspondences while performing autocalibration.
void	cv::sfm::reconstruct (const std::vector< String > images, OutputArray Ps, OutputArray points3d, InputOutputArray K, bool is_projective=false)
	Reconstruct 3d points from 2d images while performing autocalibration.
void	cv::sfm::reconstruct (const std::vector< String > images, OutputArray Rs, OutputArray Ts, InputOutputArray K, OutputArray points3d, bool is_projective=false)
	Reconstruct 3d points from 2d images while performing autocalibration.

Detailed Description

Note

• Notice that it is compiled only when Ceres Solver is correctly installed.
  Check installation instructions in the following tutorial: SFM module installation

Function Documentation

void cv::sfm::reconstruct	(	InputArrayOfArrays	points2d,
		OutputArray	Ps,
		OutputArray	points3d,
		InputOutputArray	K,
		bool	is_projective = false
	)

Reconstruct 3d points from 2d correspondences while performing autocalibration.

Parameters

points2d	Input vector of vectors of 2d points (the inner vector is per image).
Ps	Output vector with the 3x4 projections matrices of each image.
points3d	Output array with estimated 3d points.
K	Input/Output camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\). Input parameters used as initial guess.
is_projective	if true, the cameras are supposed to be projective.

This method calls below signature and extracts projection matrices from estimated K, R and t.

Note

• Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.

void cv::sfm::reconstruct	(	InputArrayOfArrays	points2d,
		OutputArray	Rs,
		OutputArray	Ts,
		InputOutputArray	K,
		OutputArray	points3d,
		bool	is_projective = false
	)

Reconstruct 3d points from 2d correspondences while performing autocalibration.

Parameters

points2d	Input vector of vectors of 2d points (the inner vector is per image).
Rs	Output vector of 3x3 rotations of the camera.
Ts	Output vector of 3x1 translations of the camera.
points3d	Output array with estimated 3d points.
K	Input/Output camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\). Input parameters used as initial guess.
is_projective	if true, the cameras are supposed to be projective.

Internally calls libmv simple pipeline routine with some default parameters by instatiating SFMLibmvEuclideanReconstruction class.

Note

• Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.
• To see a working example for camera motion reconstruction, check the following tutorial: Camera Motion Estimation.

void cv::sfm::reconstruct	(	const std::vector< String >	images,
		OutputArray	Ps,
		OutputArray	points3d,
		InputOutputArray	K,
		bool	is_projective = false
	)

Reconstruct 3d points from 2d images while performing autocalibration.

Parameters

images	a vector of string with the images paths.
Ps	Output vector with the 3x4 projections matrices of each image.
points3d	Output array with estimated 3d points.
K	Input/Output camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\). Input parameters used as initial guess.
is_projective	if true, the cameras are supposed to be projective.

This method calls below signature and extracts projection matrices from estimated K, R and t.

Note

• The images must be ordered as they were an image sequence. Additionally, each frame should be as close as posible to the previous and posterior.
• For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images.

void cv::sfm::reconstruct	(	const std::vector< String >	images,
		OutputArray	Rs,
		OutputArray	Ts,
		InputOutputArray	K,
		OutputArray	points3d,
		bool	is_projective = false
	)

Reconstruct 3d points from 2d images while performing autocalibration.

Parameters

images	a vector of string with the images paths.
Rs	Output vector of 3x3 rotations of the camera.
Ts	Output vector of 3x1 translations of the camera.
points3d	Output array with estimated 3d points.
K	Input/Output camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\). Input parameters used as initial guess.
is_projective	if true, the cameras are supposed to be projective.

Internally calls libmv simple pipeline routine with some default parameters by instatiating SFMLibmvEuclideanReconstruction class.

Note

• The images must be ordered as they were an image sequence. Additionally, each frame should be as close as posible to the previous and posterior.
• For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images.
• To see a working example for scene reconstruction, check the following tutorial: Scene Reconstruction.