Revisiting Flipping Strategy for Learning-based Stereo Depth Estimation

Abstract

Deep neural networks (DNNs) have been widely used for stereo depth estimation, which achieve great success in performance. In this paper, we introduce a novel flipping strategy for DNN on the stereo depth estimation task. Specifically, based on a common DNN for stereo matching, we apply the flipping operation for both input stereo images, which are further fed to the original DNN. A flipping loss function is proposed to jointly train the network with the initial loss. We apply our strategy to many representative networks in both supervised and self-supervised manners. Extensive experimental results demonstrate that our proposed strategy improves the performance of these networks.