BSEF-Stereo: A Stereo Matching Model Based on Branching Strategy and Error Feedback

Iterative stereo matching remains challenging in weakly textured regions, repetitive patterns, occlusions, and object boundaries, where ambiguous correspondence cues require broad contextual reasoning while accurate reconstruction depends on preserving local structural details. Existing recurrent updaters with a fixed receptive field struggle to balance these requirements, and their initial disparity estimates may retain local geometric inconsistencies. To address these limitations, we propose BSEF-Stereo, an iterative framework that combines adaptive recurrent updating with explicit error-feedback refinement. A channel-position attention module strengthens discriminative channel and spatial cues, while a branch-strategy gated recurrent unit uses complementary small- and large-kernel branches to preserve boundary details and aggregate context in ambiguous regions. An error-aware refinement module subsequently exploits reprojection error and image guidance to correct the initial disparity map. Experiments on Scene Flow, KITTI 2012, KITTI 2015, and Middlebury demonstrate competitive performance across synthetic, outdoor, and indoor scenes. BSEF-Stereo achieves 0.41 px EPE and 2.27% D1 on Scene Flow and a D1-all error of 1.48% on KITTI 2015. Ablation and sensitivity studies verify the complementary contributions of the three modules and support the selected design settings.