# Learned Geometric Edges

Stereo matching error concentrates at depth boundaries: on IGEV, geometric edge pixels are a small fraction of evaluated pixels yet contribute about half of total absolute disparity error. E3Stereo learns geometric edges from disparity supervision and uses the same edge map both to guide full-resolution disparity residual correction and to weight boundary errors during training, jointly optimized with the stereo backbone. On Scene Flow it reduces boundary end-point error by 13.4%, 14.3% and 9.5% for RAFT-Stereo, IGEV and MonSter while lowering overall error, but direct transfer to real datasets is worse than IGEV and only target-domain fine-tuning brings gains across all reported ETH3D and KITTI metrics.

Stereo matching error concentrates at depth boundaries: on IGEV, geometric edge pixels are a small fraction of evaluated pixels yet contribute about half of total absolute disparity error. E3Stereo learns geometric edges from disparity supervision and uses the same edge map both to guide full-resolution disparity residual correction and to weight boundary errors during training, jointly optimized with the stereo backbone. On Scene Flow it reduces boundary end-point error by 13.4%, 14.3% and 9.5% for RAFT-Stereo, IGEV and MonSter while lowering overall error, but direct transfer to real datasets is worse than IGEV and only target-domain fine-tuning brings gains across all reported ETH3D and KITTI metrics.