Abstract: Stereo vision is an important means to acquire stereoscopic perception of the real world, which is widely used in autonomous driving and other fields. Stereo matching algorithm serves as the basis for the binocular system to realize perception. This algorithm matches the images captured by the left and right cameras and calculates the depth map, thus providing coordinates for the 3D modeling. There are four stages of stereo matching algorithm, including cost computation, cost aggregation, disparity selection, and disparity refinement. This literature survey summarized the development of stereo matching algorithm from such two research fields as artificial feature and deep learning in the past 20 years, evaluated the accuracy and time complexity, and presented the challenges and prospects of the development of stereo matching algorithms.

Key words: stereo matching, stereo vision, stereo perception, deep learning, computer vision