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Global and interactive graph channel attention for robust stereo matching
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DOI：10.1016/j.image.2026.117491.png)
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Abstract
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Current learning-based stereo matching is generally poor in adaptively exploring the robust and salient features at different scenes, leading to ambiguity of matching, especially in challenging areas. To tackle this problem, inspired by the global representation of the graph, we propose a Graph Channel Attention (GCA) to globally and interactively learn binocular attention for robust stereo matching, instead of traditional separate local monocular attention. We first construct a 2D binocular graph structure with left and right subgraphs, where the left and right channel information can globally interact. After that, our interactive graph inference with cross interaction and inner aggregation is proposed to improve the linkage inference between and within binocular graphs, which can consider global and interactive attention information like real human eyes. Thus, our GCA alters the channel attention from traditional 1D to binocular 2D, which can imitate the global interaction and attention ability of real human eyes. Finally, we utilize the GCA into stereo matching, and experiment results show that our method demonstrates state-of-the-art performance on KITTI 2012/2015 and Middlebury Stereo Evaluation v.3.
Keywords:
Graph channel attention
Stereo matching
Binocular graph
Global interaction
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