Trans2Occ: Voxel Occupancy Estimation and Grasp for Transparent Objects from Simulation to Reality
Trans2Occ predicts voxel occupancy of transparent objects from single-view RGB images, achieving 85% grasp success rate.
Key Findings
Methodology
The method predicts voxel occupancy of transparent objects using single-view RGB images, avoiding the complexity of multi-view reconstruction and depth completion. A simulation pipeline generates large-scale training data, and a geometry-aware model predicts voxel occupancy to support downstream robotic grasping tasks.
Key Results
- In simulation, Trans2Occ achieved a 95.39% mIoU, significantly outperforming existing methods.
- In real-world settings, the model achieved a 71.67% grasp success rate without fine-tuning, demonstrating strong generalization.
- Ablation studies confirmed that voxel occupancy prediction is crucial for geometric understanding of transparent objects.
Significance
This research provides a scalable and effective solution for robotic perception and manipulation of transparent objects. By predicting voxel occupancy from single-view RGB images, it addresses the limitations of multi-view and depth sensor-based methods, advancing robotic vision technology.
Technical Contribution
Compared to existing methods, Trans2Occ directly predicts voxel occupancy from single-view RGB images, simplifying data acquisition and processing. Its geometry-aware model, trained on simulation-generated data, shows excellent sim-to-real transferability.
Novelty
Trans2Occ is the first to directly predict voxel occupancy of transparent objects from single-view RGB images, avoiding reliance on multi-view and depth sensors, offering a novel geometric perception approach.
Limitations
- In complex lighting conditions, the model's prediction accuracy may decrease, requiring further optimization.
- The model may not fully capture the details of complex geometric structures.
Future Work
Future research could explore more complex scenes and object shapes, as well as applications on different robotic platforms.
AI Executive Summary
Perception and manipulation of transparent objects have long been challenging in robotics, with traditional methods relying on multi-view reconstruction or depth completion, which are difficult to apply in practice. Trans2Occ offers a novel solution by predicting voxel occupancy of transparent objects from single-view RGB images. The method generates large-scale training data in simulation, and the model predicts voxel occupancy based on geometric perception, supporting downstream robotic grasping tasks. Experimental results show that Trans2Occ performs excellently in both simulation and real-world environments, significantly improving the success rate of grasping transparent objects. Although there is room for improvement in complex lighting conditions, this method provides new insights for transparent object perception.
Deep Analysis
Background
Transparent objects are prevalent in everyday life and scientific experiments, such as glassware and plastic containers. However, due to the refraction and reflection properties of transparent objects, traditional depth sensors struggle to accurately perceive their geometry. Existing methods often rely on multi-view reconstruction or depth completion, but these approaches have limitations in practical applications.
Core Problem
Perception and manipulation of transparent objects are challenging in robotics. Due to the optical properties of transparent materials, depth sensors often fail to provide reliable geometric information, posing challenges for robots in manipulating transparent objects.
Innovation
Trans2Occ predicts voxel occupancy of transparent objects directly from single-view RGB images, avoiding reliance on multi-view and depth sensors. The method generates large-scale training data in simulation, and the model predicts voxel occupancy based on geometric perception, supporting downstream robotic grasping tasks.
Methodology
- �� Use single-view RGB images as input, avoiding the complexity of multi-view reconstruction.
- �� Construct a simulation pipeline to generate paired RGB images and voxel occupancy annotations.
- �� Predict voxel occupancy using a geometry-aware model to support downstream grasping tasks.
- �� Propose a rule-based grasping strategy to validate the effectiveness of voxel occupancy prediction.
Experiments
Experiments were conducted in both simulation and real-world environments, using training data generated by Sim-Trans3D. The model was trained under multiclass and binary settings, with evaluation metrics including IoU and mIoU. Results show that Trans2Occ excels in geometric understanding of transparent objects.
Results
Trans2Occ achieved a 95.39% mIoU in simulation, significantly outperforming existing methods. In real-world settings, the model achieved a 71.67% grasp success rate without fine-tuning, demonstrating strong generalization.
Applications
The method can be applied in laboratory automation, industrial handling, and other scenarios requiring manipulation of transparent objects. Its accurate perception and manipulation capabilities for transparent objects will drive the application of robotic technology in various fields.
Limitations & Outlook
In complex lighting conditions, the model's prediction accuracy may decrease, requiring further optimization. The model may not fully capture the details of complex geometric structures. Future research could explore more complex scenes and object shapes, as well as applications on different robotic platforms.
Plain Language Accessible to non-experts
Imagine you're in a kitchen trying to use a robot to grab a transparent glass. Traditional methods need multiple angles to reconstruct the shape of the glass, like looking at it from different sides to know its shape. Trans2Occ is like a smart assistant that only needs one look to understand the rough shape of the glass, helping the robot grab it accurately. It trains on data generated in a simulation, like practicing in a virtual kitchen, and performs well in a real kitchen.
ELI14 Explained like you're 14
Hey, imagine you're playing a game where you need a robot to grab a transparent water bottle. Traditional methods are like needing to take photos from multiple angles to know its shape. But Trans2Occ is like a super smart assistant that only needs one glance to understand the rough shape of the bottle, helping the robot grab it accurately. It trains on data generated in a simulation, like practicing in a virtual world, and performs well in the real world. Isn't that cool?
Glossary
Voxel Occupancy
Voxel occupancy refers to whether a voxel in 3D space is occupied by an object.
Used to predict the geometry of transparent objects.
Single-view RGB Image
A color image captured from a single viewpoint.
Used as input for predicting voxel occupancy.
Geometry-aware Model
A model capable of understanding the geometric shape of objects.
Used for predicting voxel occupancy of transparent objects.
Sim-to-Real
Application of a model trained in simulation to real-world environments.
Evaluates the model's generalization ability.
Grasp Strategy
A method for determining how a robot should grasp an object.
Based on voxel occupancy prediction for grasping.
Open Questions Unanswered questions from this research
- 1 How to improve prediction accuracy under complex lighting conditions? Current methods perform poorly under such conditions, requiring further research.
- 2 How to capture the complex geometric details of objects? Existing models may not fully capture complex structures.
Applications
Immediate Applications
Laboratory Automation
Robots can automatically manipulate transparent glassware in laboratories, improving experimental efficiency.
Long-term Vision
Industrial Handling
In industrial production, robots can more efficiently handle transparent materials, improving production efficiency.
Abstract
Transparent objects remain challenging for robotic perception due to unreliable depth sensing caused by refraction and reflection. While prior approaches rely on multi-view reconstruction or depth completion, they are often difficult to scale or deploy in real-world robotic systems. In this paper, we present a practical framework for transparent object perception and manipulation based on single-view RGB input. Our approach predicts voxel-space occupancy directly from a single image, providing a geometry-aware representation that supports downstream robotic grasping. To enable large-scale training, we construct a simulation pipeline that generates paired RGB images and voxel occupancy annotations under diverse materials and lighting conditions. We demonstrate that the predicted occupancy representation is robust to domain shifts and transfers effectively from simulation to real-world robotic setups without fine-tuning. A simple rule-based grasping strategy built on top of the occupancy further achieves reliable grasp performance on transparent objects. Extensive experiments in both simulation and real-world environments show that our framework provides accurate 3D understanding and enables practical manipulation of transparent objects. These results suggest that single-view occupancy prediction offers a scalable and effective solution for transparent object perception in robotics.