Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation

arXiv cs.RO / 4/21/2026

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Key Points

The paper addresses how indoor robot navigation is degraded by glass surfaces that heavily corrupt depth sensor readings.
It proposes a training-free method that uses depth foundation models (e.g., Depth Anything 3) as a structural prior but adds a robust local RANSAC-based alignment to recover absolute metric scale.
The approach aims to prevent contamination from incorrect glass depth measurements while fusing foundation-model priors with raw RGB-D depth.
The authors introduce GlassRecon, a new RGB-D dataset with geometrically derived ground-truth for glass regions, and report consistent improvements over existing baselines, particularly under severe depth corruption.
The dataset and code are planned for public release via the provided GitHub repository.

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 \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.