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Robo3R: Enhancing Robotic Manipulation with Accurate Feed-Forward 3D Reconstruction

Sizhe Yang, Linning Xu, Hao Li, Juncheng Mu, Jia Zeng, Dahua Lin, Jiangmiao Pang
Shanghai AI Laboratory, The Chinese University of Hong Kong, University of Science and Technology of China, Tsinghua University

In Submission

arXiv Homepage

🔥 Highlight

Robo3R enables manipulation-ready 3D reconstruction from RGB frames in real time.

By achieving accurate metric-scale 3D geometry in the canonical robot frame, Robo3R eliminates the need for depth sensors and calibration, while improving accuracy and robustness in challenging manipulation scenarios.

These features lead to notable improvements in downstream applications such as imitation learning, sim-to-real transfer, grasp synthesis, and collision-free motion planning.

framework

📢 The code will be released after the paper is accepted. Stay tuned!

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