Calculates a fundamental matrix from the corresponding points in two images

F = cv.findFundamentalMat(points1, points2)
[F, mask] = cv.findFundamentalMat(...)
[...] = cv.findFundamentalMat(..., 'OptionName', optionValue, ...)

Input

• points1 Cell array of N points from the first image, or numeric array Nx2/Nx1x2/1xNx2. The point coordinates should be floating-point (single or double precision).
• points2 Cell array or numeric array of the second image points of the same size and format as points1.

Output

• F Fundamental matrix, 3x3 (or 9x3 in some cases, see below).
• mask Optional output mask set by a robust method (RANSAC or LMedS), indicates inliers. Vector of same length as number of points.

Options

• Method Method for computing a fundamental matrix. One of:
  • 7Point for a 7-point algorithm. N = 7.
  • 8Point for an 8-point algorithm. N >= 8.
  • Ransac for the RANSAC algorithm. N >= 8. (default) It needs at least 15 points. 7-point algorithm is used.
  • LMedS for the LMedS least-median-of-squares algorithm. N >= 8. 7-point algorithm is used.
• RansacReprojThreshold Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar line in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise. default 3.0
• Confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level of confidence (probability) that the estimated matrix is correct. In the range 0..1 exclusive. default 0.99

The epipolar geometry is described by the following equation:

[p2;1]^T * F * [p1;1] = 0

where F is a fundamental matrix, p1 and p2 are corresponding points in the first and the second images, respectively.

The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions (9x3 matrix that stores all 3 matrices sequentially).

The calculated fundamental matrix may be passed further to cv.computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to cv.stereoRectifyUncalibrated to compute the rectification transformation.

Example

Estimation of fundamental matrix using the RANSAC algorithm:

% initialize the points here
points1 = {[1,1],[3,1],[5,1],...}
points2 = {[2,3],[4,3],[6,3],...}
% estimate fundamental matrix
[F, mask] = cv.findFundamentalMat(points1, points2, 'Method','Ransac');