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 pre-processing step for computing disparity or creating anaglyph images.
|3-D locations of undistorted matching points in stereo images|
|Correct image for lens distortion|
|Correct point coordinates for lens distortion|
|Camera projection matrix|
|Disparity map between stereo images|
|Uncalibrated stereo rectification|
|Rectify a pair of stereo images|
|Reconstruct 3-D scene from disparity map|
|Object for storing stereo camera system parameters|
Specify pixel Indices, spatial coordinates, and 3-D coordinate systems
Calibrate a stereo camera, which you can then use to recover depth from images.
This example shows how to detect people in video taken with a calibrated stereo camera and determine their distances from the camera.
This example shows how to use the MATLAB® Coder™ to generate C code for a MATLAB function, which uses the
stereoParameters object produced by Stereo Camera Calibrator app or the