Deep Contextual Structure and Semantic Feature Enhancement Stereo Network

Depth estimation is one of the fundamental tasks of computer vision. Stereo matching is the most critical step to obtain the accurate depth information through stereo vision. At present, thin structure regions, depth discontinuity regions, and large textureless regions are still the difficult issues for stereo matching. To address the blur in thin structure regions and the dilation in depth discontinuity regions, the contextual structure enhancing module is proposed to enhance the extraction ability for local contextual features of the feature extraction network. To reduce the matching ambiguity in large textureless regions, the semantic feature enhancing module is proposed to enhance the aggregation ability for semantic features of the cost aggregation network. Extensive experiment results show that the proposed stereo network perform well in thin structure regions, depth discontinuity regions and large textureless regions and has achieved excellent performance on Scene Flow datasets, KITTI 2012 datasets, KITTI 2015 datasets and Middlebury datasets.