Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation

2026-04-20

AI Summary

This work addresses the challenge of severe depth sensor distortions caused by glass surfaces in indoor robot navigation, which compromise environment reconstruction and path planning. We propose a training-free fusion framework that, for the first time, leverages foundation depth models—such as Depth Anything V3—as structural priors, integrated with a local RANSAC alignment strategy to effectively reject erroneous depth measurements in glass regions and recover metrically accurate geometry. To support this research, we introduce GlassRecon, the first RGB-D dataset featuring geometrically derived ground-truth annotations for glass surfaces. Experimental results demonstrate that our method significantly outperforms existing approaches in depth distortion scenarios, achieving accurate glass geometry reconstruction while preserving metric consistency. Both the code and the GlassRecon dataset are publicly released.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Other Foundations of Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• glass surface
• robot navigation
• depth sensor
• surface reconstruction
• sensor corruption

Innovation

• depth prior
• glass surface reconstruction
• RANSAC-based alignment
• metric scale recovery
• RGB-D dataset