新的注意力机制提高了立体匹配的准确性和效率

报道来源 [3]

1. arXiv cs.CV TIER_1 English(EN) · Subrahmanyam Murala · 2026-06-30 13:20

   LiteMatch: Lightweight Zero-Shot Stereo Matching via Cost Volume Stabilization

   Despite rapid progress in learning-based stereo matching, high accuracy is often achieved at the cost of heavy backbones and computationally intensive 3D cost volume processing, resulting in substantial memory and runtime overhead. More critically, these methods frequently strugg…

2. arXiv cs.CV TIER_1 English(EN) · Tingman Yan, Tao Liu, Chenghao Li, Quanli Liu, Xilian Yang, Qunfei Zhao, Zeyang Xia · 2026-06-30 04:00

   MatchAttention：将显式匹配约束嵌入注意力机制以实现高效立体匹配

   arXiv:2510.14260v3 Announce Type: replace Abstract: Standard attention mechanisms are not well suited to stereo matching. Global attention scales quadratically and provides no explicit matching constraint, while local attention is efficient but loses long-range correspondences. W…

3. arXiv cs.CV TIER_1 English(EN) · Jiahao Li, Xinhong Chen, Zhengmin Jiang, Cheng Huang, Yung-Hui Li, Jianping Wang · 2026-06-30 04:00

   带法线的几何增强高效注意力调优用于鲁棒立体匹配

   arXiv:2604.09142v2 Announce Type: replace Abstract: Despite remarkable advances in image-driven stereo matching over the past decade, Synthetic-to-Realistic ZeroShot (Syn-to-Real) generalization remains an open challenge. This suboptimal generalization performance mainly stems fr…