Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation
Indoor robot navigation is often compromised by glass surfaces, which severely corrupt depth sensor measurements. While foundation models like Depth Anything 3 provide excellent geometric priors, they lack an absolute metric scale. We propose a training-free framework that leverages depth foundation models as a structural prior, employing a robust local RANSAC-based alignment to fuse it with raw sensor depth. This naturally avoids contamination from erroneous glass measurements and recovers an accurate metric scale. Furthermore, we introduce \ti{GlassRecon}, a novel RGB-D dataset with geometrically derived ground truth for glass regions. Extensive experiments demonstrate that our approach...
| Authors | Jiamin Zheng, Jingwen Yu, Guangcheng Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 April 2026 |
| Last updated at source | 23 July 2026 |
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