Learning Cross-View Semantic Priors for Single-Reference Unseen Object Pose Estimation
Single-reference unseen object 6D pose estimation reduces object onboarding by estimating poses of arbitrary novel objects from only one reference view. Recent correspondence-based pipelines have achieved robust performance with vision foundation model (VFM) features. However, they typically treat these features as intra-view descriptors, leaving dense visual-semantic cues, including appearance, structure, and context, insufficiently exchanged across views before geometric decoding. Consequently, the decoded point features may lack joint semantic and geometric discriminability, making correspondence estimation still difficult in challenging cases. Instead of processing features independen...
| Authors | Jiahong Chen, Jinghao Wang, Ziwen Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 June 2026 |
| Last updated at source | 25 June 2026 |
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