Parallax Contextual Representations For Stereo Matching

In this work, we study the context aggregation in stereo matching from a new parallax perspective. Unlike previous works, we propose to characterize and augment a pixel with its parallax contextual representation (PCR), which has not been explored before. We also propose a new concept called disparity prototype to describe the overall representation of a disparity plane. Our proposed PCR module consists of three steps: 1) divide disparity planes for a rough estimation of disparity; 2) estimate the disparity prototypes for each disparity plane; 3) derive PCR-augmented representations with disparity prototypes. Extensive experiments on various datasets using different networks validate the effectiveness of our proposal.