arXiv 2606.017772026-06-01cs.RO

Trans2Occ: 从仿真到现实的透明物体体素占用估计与抓取

Trans2Occ: Voxel Occupancy Estimation and Grasp for Transparent Objects from Simulation to Reality

Shanghai AI Laboratory（上海人工智能实验室）；SUSTech（南方科技大学）；CUHK（香港中文大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Oxford（牛津大学）；Beihang University（北京航空航天大学）；Nanyang Technological University（南洋理工大学）；University of Science and Technology of China（中国科学技术大学）

Yixuan Yang, Sha Zhang, Rui Li, Zhenfei Yin, Xinzhu Ma, Yiran Qin, Lei Bai, Xudong Xu, Shilin Shan, Wangmeng Zuo, Yanyong Zhang, Wanli Ouyang, Feng Zheng, Shixi… 展开作者

Yixuan Yang, Sha Zhang, Rui Li, Zhenfei Yin, Xinzhu Ma, Yiran Qin, Lei Bai, Xudong Xu, Shilin Shan, Wangmeng Zuo, Yanyong Zhang, Wanli Ouyang, Feng Zheng, Shixiang Tang, Dongzhan Zhou

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AI总结：

提出基于单视图RGB输入的体素占用预测框架，结合仿真数据生成与规则抓取策略，实现透明物体的鲁棒3D感知与操作。

摘要：

透明物体由于折射和反射导致的深度感知不可靠，对机器人感知构成挑战。先前的方法依赖多视图重建或深度补全，但往往难以在真实机器人系统中扩展或部署。本文提出一个基于单视图RGB输入的透明物体感知与操作实用框架。我们的方法直接从单张图像预测体素空间占用，提供支持下游机器人抓取的几何感知表示。为实现大规模训练，我们构建了一个仿真流水线，在不同材质和光照条件下生成配对的RGB图像和体素占用标注。我们证明预测的占用表示对领域偏移具有鲁棒性，并能从仿真有效迁移到真实机器人设置，无需微调。基于占用构建的简单规则抓取策略进一步实现了透明物体的可靠抓取性能。在仿真和真实环境中的大量实验表明，我们的框架提供了准确的3D理解，并实现了透明物体的实用操作。这些结果表明，单视图占用预测为机器人中的透明物体感知提供了一种可扩展且有效的解决方案。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。