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
$1^{st}$ on KITTI 2012, KITTI 2015, ETH3D, and Middlebury leaderboards among
all published methods. Code is available at
this https URL

基于选择性循环单元（SRU）和上下文空间注意力（CSA）模块的选择性立体匹配方法能够同时捕捉边缘的高频信息和平滑区域的低频信息，以减少细节丢失、边缘模糊和纹理区域的错误匹配。