BRD-Stereo: Bilateral Reliability Distillation for Lightweight Zero-Shot Stereo Matching

Stereo foundation models provide strong cross-domain generalization but remain computationally expensive for real-time and resource-constrained deployment. We propose BRD-Stereo, a bilateral reliability distillation framework that transfers foundation-model stereo knowledge to a lightweight BANet-style student. BRD-Stereo treats reciprocal left–right correspondence after geometric warping as an exchange symmetry and uses localized symmetry violations from occlusion or mismatching to reduce teacher reliability. BRD-Stereo models the spatial reliability of teacher supervision and aligns different forms of teacher knowledge with the student’s detail/smooth aggregation structure. The detail branch receives boundary- and gradient-aware supervision, while the smooth branch is guided by low-frequency disparity consistency and local plane regularization. Reliability cues from left–right consistency, image disparity edge agreement, local regularity, occlusion-aware validity, and available teacher confidence weight the pseudo-label objectives. Sliced score distribution distillation transfers a locally softened disparity target around the teacher prediction without requiring full teacher cost volume supervision. Experiments on Scene Flow, KITTI, ETH3D, and Middlebury show consistent gains over plain pseudo-label distillation, with the largest improvements concentrated in boundary, occlusion, weak-texture, and large-disparity regions while preserving BANet-scale inference efficiency.