LAUSR

Abstract Convolutional neural networks (CNNs) have been widely used in end-to-end stereo matching networks in recent years. However, most stereo networks are not robust to variations in the environment and thus are difficult to be extended to practical applications. In this paper, an inherent factor that hinders the adaptive performance of stereo matching networks is first determined. Then we propose a domain-adaptive feature extractor (DAFE) that can extract the features of images on different domains and a feature normalization method to reduce the variances of features in across-domain situations. Moreover, the influence of various modules on the performance of the domain-adaptive network (DANet) is investigated. When trained on Sceneflow data and generalized to the real test sets, the method performs significantly better than state-of-the-art models and even better than some latest disparity networks fine-tuned on the target domain.