稀疏LiDAR下用插值预填充，提升立体匹配精度
Leveraging Sparse LiDAR for RAFT-Stereo: A Depth Pre-Fill Perspective
- 用插值法填补稀疏LiDAR的初始视差图，提升引导效果
- 在仅数百个点/帧的稀疏条件下，性能显著优于现有方法
- 适合自动驾驶、机器人等低密度LiDAR场景应用
本文研究在RAFT-Stereo框架中利用LiDAR引导以提升立体匹配精度，核心在于将精确的LiDAR深度注入初始视差图。我们发现当LiDAR点云稀疏（如每帧仅几百个点）时，引导效果急剧下降，并从信号处理角度提出新解释。基于此，提出一种简单有效的解决方案：预先插值填充稀疏的初始视差图。有趣的是，该策略在早期融合中注入LiDAR深度时同样有效，但机制不同，需采用不同的插值方式。结合两种方案，所提出的GRAFT-Stereo在多种数据集上，于稀疏LiDAR条件下显著超越现有方法。本研究旨在启发更高效的LiDAR引导立体匹配方法。
原文摘要 · Abstract (English)
We investigate LiDAR guidance within the RAFT-Stereo framework, aiming to improve stereo matching accuracy by injecting precise LiDAR depth into the initial disparity map. We find that the effectiveness of LiDAR guidance drastically degrades when the LiDAR points become sparse (e.g., a few hundred points per frame), and we offer a novel explanation from a signal processing perspective. This insight leads to a surprisingly simple solution that enables LiDAR-guided RAFT-Stereo to thrive: pre-filling the sparse initial disparity map with interpolation. Interestingly, we find that pre-filling is also effective when injecting LiDAR depth into image features via early fusion, but for a fundamentally different reason, necessitating a distinct pre-filling approach. By combining both solutions, the proposed Guided RAFT-Stereo (GRAFT-Stereo) significantly outperforms existing LiDAR-guided methods under sparse LiDAR conditions across various datasets. We hope this study inspires more effective LiDAR-guided stereo methods.
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