Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation
Enhancing glass surface reconstruction using depth prior improves robot navigation accuracy.
Key Findings
Methodology
The paper proposes a training-free framework that leverages modern monocular depth estimation networks to provide structural depth priors, which are aligned with sensor metric scales using a robust local RANSAC-based alignment. This method calculates scale-shift pairs from local image patches and validates them globally, avoiding contamination from erroneous glass measurements and preserving structural integrity.
Key Results
- In experiments, the proposed method significantly outperforms existing baselines under severe sensor depth corruption, particularly on the hard subset, reducing AbsRel error by over 46%.
- Compared to global alignment baselines, the local RANSAC alignment method performs better across almost all networks and subsets, especially on the hard subset where glass causes severe depth corruption.
- Experiments on ScanNet++ and real-world datasets demonstrate that the method can recover the planar structure of glass surfaces, generating complete and geometrically consistent maps.
Significance
This research addresses the long-standing challenge of accurate depth measurement on glass surfaces, significantly enhancing the safety and accuracy of indoor robot navigation. It not only provides a new research direction in academia but also offers new possibilities for robot navigation and scene understanding in glass-rich environments in the industry.
Technical Contribution
The technical contribution lies in proposing a training-free framework that combines monocular depth priors with sensor metric depth through local RANSAC alignment, achieving high-precision glass surface reconstruction. Compared to existing methods, this approach requires no specialized glass training data or hardware, offering high generalizability and deployability.
Novelty
The paper is the first to combine modern monocular depth estimation network structural priors with sensor metric depth through local RANSAC alignment for glass surface reconstruction. Compared to existing methods, this approach requires no specialized training data or hardware, offering greater flexibility and applicability.
Limitations
- If the depth prior fails to accurately predict the geometry of glass regions, the method may fail, such as when the prior incorrectly estimates depth corresponding to background objects behind the glass.
- When glass regions occupy most of the image and sensor depth returns erroneous but valid depth values, it may lead to alignment biased towards incorrect measurements.
- The local RANSAC alignment assumes that randomly sampled pixels are mainly from regions where sensor depth is reliable, which may not hold in some cases.
Future Work
Future research directions include expanding the dataset to cover more types of glass and scenes, enhancing the performance of depth priors. Additionally, incorporating uncertainty estimation into the alignment process could improve robustness by adaptively weighting pixels based on their reliability. Finally, extending the method to leverage temporal constraints across sequential RGB-D frames may resolve geometric ambiguities in challenging cases where single-frame priors prove insufficient.
AI Executive Summary
Accurate perception of glass surfaces is critical for safe robot navigation in indoor environments. However, standard RGB-D depth sensors struggle with the transparency and reflectivity of glass, often producing invalid data or incorrectly capturing background objects. Existing solutions, such as specialized LiDAR, complementary sensors, or glass-specific neural networks, are often constrained by environmental conditions, high hardware costs, or poor generalizability to unseen domains.
Modern monocular depth estimation models, such as Depth Anything V3, provide powerful structural priors but fail to deliver accurate metric scale on their own. To bridge this gap, the paper proposes a modular, training-free pipeline that leverages a modern affine-invariant monocular network to obtain a structural depth prior. This prior is then aligned to the sensor's metric scale using a novel local RANSAC-based alignment. By calculating scale-shift pairs after local sampling from image patches and validating them globally, the method inherently avoids contamination from erroneous sensor measurements on glass, preserving the prior's structural fidelity.
To rigorously evaluate the approach, the authors introduce GlassRecon, a dedicated dataset featuring glass instances. Assuming most indoor glass is planar, ground-truth depth is generated using geometric constraints derived from reliable coplanar surfaces. The dataset features 'easy' and 'hard' subsets, enabling nuanced evaluation. The main contributions are summarized as follows:
• A glass surface depth completion method that combines monocular depth priors with local RANSAC alignment.
• A new RGB-D dataset with geometrically derived ground truth and glass region annotations.
• Extensive experiments demonstrating that the method consistently outperforms global alignment baselines and metric depth prediction networks, with particularly significant gains on hard samples.
The method not only performs well in experiments but also shows potential in practical applications. Experiments on ScanNet++ and real-world datasets demonstrate that the method can recover the planar structure of glass surfaces, generating complete and geometrically consistent maps. These improved reconstructions can benefit a variety of robotics applications, including semantic mapping, obstacle avoidance, and safe navigation in settings where glass surfaces would otherwise be misperceived. Nonetheless, the method has limitations in certain scenarios, such as when the depth prior fails to accurately predict the geometry of glass regions. Future research directions include expanding the dataset to cover more types of glass and scenes, and incorporating uncertainty estimation into the alignment process to improve robustness.
Deep Analysis
Background
Accurate perception of glass surfaces is critical for safe robot navigation in indoor environments. However, standard RGB-D depth sensors struggle with the transparency and reflectivity of glass, often producing invalid data or incorrectly capturing background objects. Existing solutions, such as specialized LiDAR, complementary sensors, or glass-specific neural networks, are often constrained by environmental conditions, high hardware costs, or poor generalizability to unseen domains. Modern monocular depth estimation models, such as Depth Anything V3, provide powerful structural priors but fail to deliver accurate metric scale on their own. To bridge this gap, the paper proposes a modular, training-free pipeline that leverages a modern affine-invariant monocular network to obtain a structural depth prior. This prior is then aligned to the sensor's metric scale using a novel local RANSAC-based alignment. By calculating scale-shift pairs after local sampling from image patches and validating them globally, the method inherently avoids contamination from erroneous sensor measurements on glass, preserving the prior's structural fidelity.
Core Problem
Accurate perception of glass surfaces is critical for safe robot navigation in indoor environments. However, standard RGB-D depth sensors struggle with the transparency and reflectivity of glass, often producing invalid data or incorrectly capturing background objects. Existing solutions, such as specialized LiDAR, complementary sensors, or glass-specific neural networks, are often constrained by environmental conditions, high hardware costs, or poor generalizability to unseen domains. Modern monocular depth estimation models, such as Depth Anything V3, provide powerful structural priors but fail to deliver accurate metric scale on their own. To bridge this gap, the paper proposes a modular, training-free pipeline that leverages a modern affine-invariant monocular network to obtain a structural depth prior. This prior is then aligned to the sensor's metric scale using a novel local RANSAC-based alignment. By calculating scale-shift pairs after local sampling from image patches and validating them globally, the method inherently avoids contamination from erroneous sensor measurements on glass, preserving the prior's structural fidelity.
Innovation
The paper proposes a glass surface depth completion method that combines monocular depth priors with local RANSAC alignment. Compared to existing methods, this approach requires no specialized glass training data or hardware, offering high generalizability and deployability. By achieving high-precision glass surface reconstruction through local RANSAC alignment, the method avoids contamination from erroneous glass measurements, preserving structural integrity. Compared to existing methods, this approach requires no specialized training data or hardware, offering greater flexibility and applicability.
Methodology
The paper proposes a glass surface depth completion method that combines monocular depth priors with local RANSAC alignment. Compared to existing methods, this approach requires no specialized glass training data or hardware, offering high generalizability and deployability. By achieving high-precision glass surface reconstruction through local RANSAC alignment, the method avoids contamination from erroneous glass measurements, preserving structural integrity. Compared to existing methods, this approach requires no specialized training data or hardware, offering greater flexibility and applicability.
Experiments
To evaluate the method, the authors introduce GlassRecon, a dedicated dataset featuring glass instances. Assuming most indoor glass is planar, ground-truth depth is generated using geometric constraints derived from reliable coplanar surfaces. The dataset features 'easy' and 'hard' subsets, enabling nuanced evaluation. The main contributions are summarized as follows:
- �� A glass surface depth completion method that combines monocular depth priors with local RANSAC alignment.
- �� A new RGB-D dataset with geometrically derived ground truth and glass region annotations.
- �� Extensive experiments demonstrating that the method consistently outperforms global alignment baselines and metric depth prediction networks, with particularly significant gains on hard samples.
Results
In experiments, the proposed method significantly outperforms existing baselines under severe sensor depth corruption, particularly on the hard subset, reducing AbsRel error by over 46%. Compared to global alignment baselines, the local RANSAC alignment method performs better across almost all networks and subsets, especially on the hard subset where glass causes severe depth corruption. Experiments on ScanNet++ and real-world datasets demonstrate that the method can recover the planar structure of glass surfaces, generating complete and geometrically consistent maps.
Applications
The method not only performs well in experiments but also shows potential in practical applications. Experiments on ScanNet++ and real-world datasets demonstrate that the method can recover the planar structure of glass surfaces, generating complete and geometrically consistent maps. These improved reconstructions can benefit a variety of robotics applications, including semantic mapping, obstacle avoidance, and safe navigation in settings where glass surfaces would otherwise be misperceived.
Limitations & Outlook
Nonetheless, the method has limitations in certain scenarios, such as when the depth prior fails to accurately predict the geometry of glass regions. Future research directions include expanding the dataset to cover more types of glass and scenes, and incorporating uncertainty estimation into the alignment process to improve robustness.
Plain Language Accessible to non-experts
Imagine walking around your house and suddenly bumping into a transparent glass door. You might not notice it because of its transparency, and you could even walk into it. Robots face a similar problem when navigating indoors. Their depth sensors often fail when encountering glass because the transparency and reflectivity make it difficult for the sensors to measure depth accurately. It's like trying to see through a mirror in the dark.
To help robots better recognize glass, we propose a new method. We use a technique called 'depth prior,' which is like giving the robot special glasses that help it see the outline of the glass. Then, we use a method called 'RANSAC' to correct these measurements, like adding corrective lenses to the glasses, allowing the robot to judge the depth of the glass more accurately.
With this method, robots can navigate indoors more safely, avoiding collisions with glass. This not only improves the safety of the robots but also makes them more efficient in complex indoor environments. In the future, we hope to further improve this technology so that robots can perform well with more types of glass and in more complex environments.
ELI14 Explained like you're 14
Hey there! Have you ever walked around your house and suddenly bumped into a glass door? It's pretty embarrassing, right? Well, robots have the same problem when moving around indoors! Their depth sensors often mess up when they encounter glass because it's so transparent that the sensors can't see it clearly.
To stop robots from 'bumping into glass,' scientists came up with a new method. They gave robots something called a 'depth prior,' which is like giving them super glasses that help them see the outline of the glass. Then, they used a method called 'RANSAC' to fix these measurements, like adding corrective lenses to the glasses, so the robots can judge the depth of the glass more accurately.
This way, robots can move around the house more safely and won't bump into glass anymore! This not only makes robots smarter but also helps them work better in complex indoor environments. In the future, we hope to make robots perform well with more types of glass and in more complex environments. Isn't that cool?
Glossary
Depth Prior
A depth prior is a method that uses existing depth information to assist new depth measurements. In this paper, it helps robots better recognize the depth of glass surfaces.
In this paper, depth prior is used to provide structural information to help correct sensor depth measurement errors.
RANSAC
RANSAC is an iterative algorithm used to estimate model parameters from a set of data. It finds the best model parameters through random sampling and validation.
The paper uses RANSAC to align depth priors with the sensor's metric scale.
RGB-D Sensor
An RGB-D sensor is a device that captures both color images and depth information simultaneously. It is widely used in robot navigation and 3D reconstruction.
The paper uses RGB-D sensors to obtain depth information of the environment.
Glass Reconstruction
Glass reconstruction refers to the process of recovering the geometry and depth information of glass surfaces through computational methods.
The paper proposes a new glass reconstruction method combining depth prior and RANSAC alignment.
Monocular Depth Estimation
Monocular depth estimation is a method of inferring depth information from a single image. It is often used for 3D reconstruction without depth sensors.
The paper uses monocular depth estimation to provide structural priors for glass reconstruction.
Metric Scale
Metric scale refers to the absolute unit scale used in depth measurements to ensure accuracy.
The paper recovers the metric scale of depth priors through RANSAC alignment.
Structural Prior
Structural prior refers to using geometric structure information of a scene to assist depth estimation.
The paper uses structural prior to help correct sensor depth measurement errors.
Depth Sensor
A depth sensor is a device used to measure the distance between objects and the sensor.
The paper uses depth sensors to obtain depth information of the environment.
Dataset
A dataset is a collection of data used for training and testing algorithms.
The paper introduces a new RGB-D dataset to evaluate the glass reconstruction method.
Error Correction
Error correction refers to the process of reducing or eliminating measurement errors through computational methods.
The paper achieves depth measurement error correction through RANSAC alignment.
Open Questions Unanswered questions from this research
- 1 How to improve the accuracy of glass reconstruction in more complex environments? Existing methods still have limitations in certain scenarios, especially when the depth prior fails to accurately predict the geometry of glass regions. Further research is needed to enhance the robustness of the method.
- 2 How to maintain high performance across various types of glass and scenes? Existing datasets may not cover all possible types of glass and scenes, and expanding the dataset will help improve the generalizability of the method.
- 3 How to improve the real-time performance of the method without increasing computational complexity? Existing methods may have limitations in computational cost, and further optimization is needed to enhance real-time performance.
- 4 How to resolve geometric ambiguities when single-frame priors are insufficient? Existing methods may not provide enough information to resolve geometric ambiguities in some cases, and further research is needed to leverage temporal constraints.
- 5 How to improve the accuracy of the method without increasing hardware costs? Existing methods may require additional hardware support to improve accuracy, and further research is needed to enhance the performance of the method without increasing hardware costs.
Applications
Immediate Applications
Indoor Robot Navigation
The method can be directly applied to indoor robot navigation, helping robots more accurately recognize glass surfaces, avoid collisions, and improve navigation safety.
3D Scene Reconstruction
By combining depth priors and RANSAC alignment, the method can be used for 3D scene reconstruction, generating more complete and geometrically consistent maps.
Semantic Mapping
The method can be used for semantic mapping, helping robots better understand objects and structures in the environment, enhancing scene understanding capabilities.
Long-term Vision
Autonomous Driving
In the future, the method can be applied to autonomous driving, helping vehicles better recognize and handle glass surfaces, improving driving safety.
Smart Home
The method can be applied to smart home devices, helping them better recognize and handle glass surfaces, enhancing the intelligence of the devices.
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.
References (20)
Depth Anything V2
Lihe Yang, Bingyi Kang, Zilong Huang et al.
Depth Anything 3: Recovering the Visual Space from Any Views
Haotong Lin, Sili Chen, J. Liew et al.
Matterport3D: Learning from RGB-D Data in Indoor Environments
Angel X. Chang, Angela Dai, T. Funkhouser et al.
ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
Chandan Yeshwanth, Yueh-Cheng Liu, M. Nießner et al.
nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping
A. Millane, Helen Oleynikova, Emilie Wirbel et al.
3D Reconstruction in the Presence of Glass and Mirrors by Acoustic and Visual Fusion
Yu Zhang, Mao Ye, Dinesh Manocha et al.
Glass Detection in Simultaneous Localization and Mapping of Mobile Robot Based on RGB-D Camera
Yin Zhao, Hao Li, Shengjian Jiang et al.
Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-Shot Metric Depth and Surface Normal Estimation
Mu Hu, Wei Yin, C. Zhang et al.
Monocular Depth Estimation for Glass Walls With Context: A New Dataset and Method
Yuan Liang, Bailin Deng, Wenxi Liu et al.
Depth Anything with Any Prior
Zehan Wang, Siyu Chen, Lihe Yang et al.
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Lihe Yang, Bingyi Kang, Zilong Huang et al.
Glass Segmentation using Intensity and Spectral Polarization Cues
Haiyang Mei, Bo Dong, Wen Dong et al.
MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
Ruicheng Wang, Sicheng Xu, Yue Dong et al.
LiDAR-Based 3-D Glass Detection and Reconstruction in Indoor Environment
Lelai Zhou, Xiaohui Sun, Chen Zhang et al.
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer
René Ranftl, Katrin Lasinger, David Hafner et al.
Detecting glass in Simultaneous Localisation and Mapping
Xun Wang, J. Wang
MonoGlass3D: Monocular 3D Glass Detection with Plane Regression and Adaptive Feature Fusion
Kai Zhang, Guoyang Zhao, Jianxin Shi et al.
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM
C. Campos, Richard Elvira, J. Rodr'iguez et al.
UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler
Luigi Piccinelli, Christos Sakaridis, Yung-Hsu Yang et al.
MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision
Ruicheng Wang, Sicheng Xu, Cassie Dai et al.