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VFM Vision Foundation Model Enables Generalizable Object Pose Estimation Kai Chen 1 , Yiyao Ma 1 , Xingyu Lin 2 , Stephen James 3 , Jianshu Zhou 1 , Yun-Hui Liu 1 , Pieter Abbeel 2 , Qi Dou 1 1 The Chinese University of Hong Kong , 2 University of California, Berkeley , 3 Dyson Robot Learning Lab Paper Code (coming soon) VFM-6D uses cost-effective synthetic data to adapt robust object representations from pre-trained vision foundation models to the task of object pose estimation. VFM-6D supports both instance-level unseen object pose estimation and category-level pose estimation for novel object categories. Abstract Object pose estimation plays a crucial role in robotic manipulation, however, its practical applicability still suffers from limited generalizability. This paper addresses the challenge of generalizable object pose estimation, particularly focusing on category-level object pose estimation for unseen o...
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