Binocular stereo vision simulates the human visual system to perceive the environment in three dimensions. The parallax between two images can be obtained by stereo-matching the corrected left and right images and calculating the scene depth based on the triangulation principle. This area has been identified as a research hotspot in computer vision and has made significant progress in the past few decades. Traditional stereo matching methods use hand-designed features that perform poorly in weak or repeated texture and occlusion areas. Recently, stereo matching methods based on deep learning have significantly progressed, showing strong application potential. In this review, we conducted a comprehensive survey on this developing field, discussed the advantages and limitations of the different methods, introduced the binocular products currently on the market, and assessed the research and application prospects in this field.