Stereo Matching
Overview
Stereo matching finds corresponding points in two camera views, usually rectified so their horizontal displacement, or disparity, can be converted into depth. Modern methods build cost volumes or correlation maps and refine disparities with recurrent updates, while attention and transformer models use wider context to handle occlusions, textureless surfaces, and thin structures. Other approaches incorporate monocular depth, geometric or foundation-model priors, self-supervision, and synthetic data to improve transfer across settings such as driving, underwater imaging, satellites, and omnidirectional cameras. The central tension is achieving accurate, generalizable depth under changing appearance and sparse supervision while keeping inference fast and compact enough for deployment.
Latest papers
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