Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation
Jiamin Zheng*, Jingwen Yu*, Guangcheng Chen, Hong Zhang
* Equal contribution.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2026.
Best Paper Finalist: 10/1,933 (Top 0.52%).
Abstract
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 GlassRecon, a novel RGB-D dataset with geometrically derived ground truth for glass regions. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art baselines, especially under severe sensor depth corruption. The dataset and related code are available at https://github.com/JMamie/GlassRecon.
Links
- Full text (PDF, v3): glassrecon-v3.pdf
- arXiv: https://arxiv.org/abs/2604.18336
- Project page: https://jarvisyjw.github.io/GlassRecon/
- Code and dataset: https://github.com/JMamie/GlassRecon