Computes a rectification transform for an uncalibrated stereo camera

[H1,H2,success] = cv.stereoRectifyUncalibrated(points1, points2, F, imageSize)
[...] = cv.stereoRectifyUncalibrated(..., 'OptionName', optionValue, ...)

Input

• points1 Array of feature points in the first image as a cell array of 2-element vectors: {[x1, y1], [x2, y2], ...} or an Nx2/Nx1x2/1xNx2 numeric array. The same formats as in cv.findFundamentalMat are supported.
• points2 The corresponding points in the second image, same size and type as points1.
• F Input 3x3 fundamental matrix. It can be computed from the same set of point pairs using cv.findFundamentalMat.
• imageSize Size of the image [w,h].

Output

• H1 3x3 rectification homography matrix for the first image.
• H2 3x3 rectification homography matrix for the second image.
• success success flag. Returns true if successfull, false otherwise.

Options

• Threshold Optional threshold used to filter out the outliers. If the parameter is greater than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which |points2{i}' * F * points1{i}| > Threshold) are rejected prior to computing the homographies. Otherwise,all the points are considered inliers. default 5

The function computes the rectification transformations without knowing intrinsic parameters of the cameras and their relative position in the space, which explains the suffix "uncalibrated". Another related difference from cv.stereoRectify is that the function outputs not the rectification transformations in the object (3D) space, but the planar perspective transformations encoded by the homography matrices H1 and H2. The function implements the algorithm [Hartley99].

Note

While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using cv.calibrateCamera. Then, the images can be corrected using cv.undistort, or just the point coordinates can be corrected with cv.undistortPoints.

References

[Hartley99]

Richard I Hartley. "Theory and practice of projective rectification". International Journal of Computer Vision, 35(2):115-127, 1999.