Stereo vision is the process of recovering depth from camera images by comparing two or more views of the same scene. The output of this computation is a 3-D point cloud, where each 3-D point corresponds to a pixel in one of the images.
Stereo image rectification projects images onto a common image plane in such a way that the corresponding points have the same row coordinates. This process is useful for stereo vision, because the 2-D stereo correspondence problem reduces to a 1-D problem. As an example, stereo image rectification is often used as a preprocessing step for computing disparity or creating anaglyph images.
|3-D locations of undistorted matching points in stereo images|
|Compute epipolar lines for stereo images|
|Determine whether image contains epipole|
|Correct image for lens distortion|
|Correct point coordinates for lens distortion|
|Compute disparity map using block matching (Since R2019a)|
|Compute disparity map through semi-global matching (Since R2019a)|
|Uncalibrated stereo rectification (Since R2022b)|
|Intersection points of lines in image and image border|
|Reconstruct 3-D scene from disparity map|
|Rectify pair of stereo images|
|Object for storing stereo camera system parameters|