EDNet++: Improving Stereo Matching with Two-Stage Combined Cost Volume and Multiscale Dynamic Attention

Abstract:

Most state-of-the-art stereo matching networks construct 4D cost volume to preserve the semantic information of the image， which increases the computational cost of the network. To solve this problem， a network named EDNet++ with a two-stage combined cost volume and a multi-scale dynamic attention is proposed. First， a correlation cost volume is constructed based on global and coarse-grained disparity search range， which is used as a guide to construct a fine-grained combined cost volume on the local disparity search range. Then， the dynamic attention mechanism based on residuals can adaptively generate spatial attention distribution according to the intermediate result information， and the effectiveness of this method is proved by the transfer experiment. The comparison experiments on various public data sets show that EDNet++ can achieve a good balance between accuracy and real-time performance compared with other methods.

Key words:stereo matching;neural network;attention mechanism