Pseudo Segmentation for Semantic Information-Aware Stereo Matching.

Stereo matching plays an important role in computer vision and robotics. Though substantial progress has been made on deep learning-based algorithms, the inherent semantic information within the ground truth of the training data for stereo matching has not been well explored. In this letter, we propose to use a pseudo segmentation sub-network to extract additional semantic information. More specifically, we divide the disparity label into groups and let each group correspond to a class for pseudo segmentation. To assist stereo matching with the semantic information obtained from pseudo segmentation, we inject the feature maps at the end of the pseudo segmentation sub-network into the cost volume that is used to infer the pixel-level disparity. To validate the effectiveness of the proposed approach, we select PSMNet (Chang and Chen, 2018)and GwcNet (Guo et al., 2019) as baselines and enhance them with the pseudo segmentation sub-network. Comprehensive experiments are carried out on the Scene Flow, KITTI 2015, and KITTI 2012 datasets, and the results show that our proposed method can improve the performance notably. [ABSTRACT FROM AUTHOR]