Disparity Estimation with Scene Depth Cues

Abstract

The cost volume plays a pivotal role in stereo matching, usually working as an optimization object. However, we find it also can provide effective scene prior to guide the disparity learning, as it reflects well the depth relationship between scenario objects. Inspired by this new perspective, we propose the CSA module, which consists of a new correlation and selection (CS) layer and a new aggregation layer. The CS layer can regulate the matching costs and re-encode the feature information into the correlation volume. The aggregation layer can preserve better the depth cues of the refined cost volume, through a convolution network and a unimodalization operation. The proposed module can be trained in a supervised manner, making the extraction of scene depth cues more accurate. Extensive experiments on the Sceneflow and KITTI datasets have demonstrated that with our module embedded, SOTA networks can achieve substantially better performance