DexOPE: 6D Object Pose Estimation in Dexterous Manipulation

Reliable 6D object pose estimation is essential for dexterous manipulation but remains highly challenging due to severe visual occlusions from multi-finger interactions. Progress has been limited by two key factors: the lack of large-scale manipulation datasets and the inability of existing methods to handle high uncertainty under occlusion. To address these challenges, we introduce DexOPE, a large-scale dataset and a physics-guided pose estimation framework. DexOPE provides a multimodal dataset with 1.5k diverse objects, an order of magnitude larger than prior work, covering simulated and real-world grasping and manipulation. To handle pose ambiguity under occlusion, we propose a Physics-Guided Score Matching approach that models the pose posterior using score-based diffusion models rather than deterministic regression. Physical constraints from hand–object interactions, including nonpenetration and tactile contact, are incorporated as guidance during sampling, enabling physically plausible pose inference even under severe or complete occlusion. Extensive experiments show that DexOPE achieves state-of-the-art performance, significantly outperforming existing methods in robustness to occlusion and generalization to unseen objects.