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.

翻译：室内机器人导航常常受到玻璃表面的影响，这些表面会严重破坏深度传感器的测量结果。尽管像Depth Anything 3这样的基础模型提供了出色的几何先验，但它们缺乏绝对的度量尺度。我们提出了一种无需训练的框架，利用深度基础模型作为结构先验，并采用基于局部RANSAC的鲁棒对齐方法将其与原始传感器深度数据融合。该方法自然避免了来自错误玻璃测量的污染，并恢复出准确的度量尺度。此外，我们引入了\ti{GlassRecon}，这是一个新颖的RGB-D数据集，其中包含针对玻璃区域的几何推导地面真值。大量实验表明，我们的方法在性能上始终优于最先进的基线方法，尤其是在传感器深度严重污染的情况下。该数据集及相关代码将在https://github.com/jarvisyjw/GlassRecon发布。