The paper "Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching" introduces a novel iterative update operator called Selective Recurrent Unit (SRU) to enhance stereo matching methods. SRU is designed to adaptively fuse hidden disparity information at multiple frequencies, addressing the limitations of traditional methods that struggle to capture both high-frequency details in edges and low-frequency information in smooth regions. The authors also introduce a Contextual Spatial Attention (CSA) module to generate attention maps that guide the fusion process, ensuring that the network can selectively focus on relevant information based on the image regions. The proposed method, collectively referred to as Selective-Stereo, is evaluated on several benchmark datasets, including KITTI 2012, KITTI 2015, ETH3D, and Middlebury, achieving state-of-the-art performance in most metrics. The paper demonstrates the effectiveness of SRU and CSA through ablation studies and comparisons with state-of-the-art methods, showing that the proposed approach significantly improves the accuracy and robustness of stereo matching.The paper "Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching" introduces a novel iterative update operator called Selective Recurrent Unit (SRU) to enhance stereo matching methods. SRU is designed to adaptively fuse hidden disparity information at multiple frequencies, addressing the limitations of traditional methods that struggle to capture both high-frequency details in edges and low-frequency information in smooth regions. The authors also introduce a Contextual Spatial Attention (CSA) module to generate attention maps that guide the fusion process, ensuring that the network can selectively focus on relevant information based on the image regions. The proposed method, collectively referred to as Selective-Stereo, is evaluated on several benchmark datasets, including KITTI 2012, KITTI 2015, ETH3D, and Middlebury, achieving state-of-the-art performance in most metrics. The paper demonstrates the effectiveness of SRU and CSA through ablation studies and comparisons with state-of-the-art methods, showing that the proposed approach significantly improves the accuracy and robustness of stereo matching.