Pseudo Segmentation for Semantic Information-Aware Stereo Matching

Shengyou Hua, Zhiyong Sun, Bo Song, Pengpeng Liang, Erkang Cheng

Abstract: 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 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.