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立体视觉

Stereo rectification, disparity, and dense 3-D reconstruction

立体视觉是侦察的过程vering 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.

Stereo Camera Calibrator display

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
estimateUncalibratedRectification Uncalibrated stereo rectification
lineToBorderPoints Intersection points of lines in image and image border
reconstructScene Reconstruct 3-D scene from disparity map
rectifyStereoImages Rectify a 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
rotationMatrixToVector Convert 3-D rotation matrix to rotation vector
rotationVectorToMatrix Convert 3-D rotation vector to rotation matrix

Topics