用深度先验修复玻璃表面，让机器人导航更准
Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation
- 用现成深度模型作结构先验，不训练直接用
- 在传感器深度严重出错时仍能恢复真实尺度
- 专为玻璃区域设计数据集，适合做导航算法研究
室内机器人导航常受玻璃表面影响，因其严重干扰深度传感器测量。尽管像Depth Anything 3这样的基础模型提供良好几何先验，但缺乏绝对度量尺度。本文提出一种无需训练的框架，利用深度基础模型作为结构先验，通过鲁棒的局部RANSAC对齐方法将其与原始传感器深度融合，自然规避了错误玻璃测量的污染，并恢复准确度量尺度。此外，我们构建了新数据集GlassRecon，包含基于几何推导的真实标签。大量实验表明，该方法在传感器深度严重失真时仍显著优于现有最优基准。代码与数据集将开源于https://github.com/jarvisyjw/GlassRecon。
原文摘要 · Abstract (English)
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 consistently outperforms state-of-the-art baselines, especially under severe sensor depth corruption. The dataset and related code will be released at https://github.com/jarvisyjw/GlassRecon.
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