Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching

Xianqi Wang, Gangwei Xu, Hao Jia, Xin Yang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 19701-19710

Stereo matching methods based on iterative optimization like RAFT-Stereo and IGEV-Stereo have evolved into a cornerstone in the field of stereo matching. However these methods struggle to simultaneously capture high-frequency information in edges and low-frequency information in smooth regions due to the fixed receptive field. As a result they tend to lose details blur edges and produce false matches in textureless areas. In this paper we propose Selective Recurrent Unit (SRU) a novel iterative update operator for stereo matching. The SRU module can adaptively fuse hidden disparity information at multiple frequencies for edge and smooth regions. To perform adaptive fusion we introduce a new Contextual Spatial Attention (CSA) module to generate attention maps as fusion weights. The SRU empowers the network to aggregate hidden disparity information across multiple frequencies mitigating the risk of vital hidden disparity information loss during iterative processes. To verify SRU's universality we apply it to representative iterative stereo matching methods collectively referred to as Selective-Stereo. Our Selective-Stereo ranks first on KITTI 2012 KITTI 2015 ETH3D and Middlebury leaderboards among all published methods. Code is available at https://github.com/Windsrain/Selective-Stereo.