# Stereo Vision

Stereo rectification, disparity, and dense 3-D reconstruction

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.

## Apps

 Camera Calibrator Estimate geometric parameters of a single camera Stereo Camera Calibrator Estimate geometric parameters of a stereo camera

## Functions

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 `triangulate` 3-D locations of undistorted matching points in stereo images `epipolarLine` Compute epipolar lines for stereo images `isEpipoleInImage` Determine whether image contains epipole `undistortImage` Correct image for lens distortion `undistortPoints` Correct point coordinates for lens distortion `disparityBM` Compute disparity map using block matching `disparitySGM` Compute disparity map through semi-global matching `estimateStereoRectification` Uncalibrated stereo rectification (Since R2022b) `lineToBorderPoints` Intersection points of lines in image and image border `reconstructScene` Reconstruct 3-D scene from disparity map `rectifyStereoImages` Rectify pair of stereo images `stereoParameters` Object for storing stereo camera system parameters
 `stereoAnaglyph` Create red-cyan anaglyph from stereo pair of images `pcshow` Plot 3-D point cloud `plotCamera` Plot a camera in 3-D coordinates
 `rotmat2vec3d` Convert 3-D rotation matrix to rotation vector (Since R2022b) `rotvec2mat3d` Convert 3-D rotation vector to rotation matrix (Since R2022b)