SA-Net: Scene-Aware Network for Cross-domain Stereo Matching

Although the recent stereo matching methods based on deep learning achieve unprecedented state-of-the-art performance, the accuracy of these approaches suffers a drastic drop when dealing with environments much different in context from those observed at training time. In this paper, we propose a novel Scene-Aware Network (SA-Net) that integrates scene information to achieve cross-domain stereo matching. Specifically, we design a Scene-Aware Module (SAM) to extract rich scene details, which can make the network with it have better generalization ability between different domain. In order to use rich scene information to perfectly guide shallow features to realize cost aggregation, we introduce a new Multi-element Feature Fusion Strategy (MFFS). Extensive quantitative and qualitative evaluations on different domain illustrate that our SA-Net achieves competitive performance and in particular obtains better ability of domain generalization.