aiqbench analysis
This research addresses a critical failure mode in embodied AI by using foundation models to reconstruct accurate depth maps through glass surfaces that typically corrupt sensor data. The proposed training-free framework combines geometric priors with robust alignment techniques to recover metric scale, significantly improving indoor robot navigation reliability. Additionally, the release of the GlassRecon dataset provides essential ground truth for evaluating and advancing vision-based robotics in complex environments.
Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation
arXiv:2604.18336v3 Announce Type: replace 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.
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 this https URL.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2604.18336 [cs.RO]
(or arXiv:2604.18336v3 [cs.RO] for this version)
Full text under the publisher's open licence · 133 words
Related coverage
GlassGuard: Verified Glass Plane Mapping for Robot Navigation
arXiv · Oct 2, 2026
SILICA: Repurposing Diffusion Priors for Joint Glass Segmentation and Depth Estimation
arXiv · Jul 28, 2026
[AINews] Claude Fable/Mythos 5.1: new SOTA model, 75% cache price cut but 70% more output tokens
Latent Space · Sep 2, 2026
GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion
arXiv · Sep 30, 2026