Deep Stereo Matching With Hysteresis Attention and Supervised Cost Volume Construction

Stereo matching disparity prediction for rectified image pairs is of great importance to many vision tasks such as depth sensing and autonomous driving. Previous work on the end-to-end unary trained networks follows the pipeline of feature extraction, cost volume construction, matching cost aggregation, and disparity regression. In this paper, we propose a deep neural network architecture for stereo matching aiming at improving the first and second stages of the matching pipeline. Specifically, we show a network design inspired by hysteresis comparator in the circuit as our attention mechanism. Our attention module is multiple-block and generates an attentive feature directly from the input. The cost volume is constructed in a supervised way. We try to use data-driven to find a good balance between informativeness and compactness of extracted feature maps. The proposed approach is evaluated on several benchmark datasets. Experimental results demonstrate that our method outperforms previous methods on SceneFlow, KITTI 2012, and KITTI 2015 datasets.