SAFAG introduces a symmetry annotation-free two-stage learning strategy for generalizable actionable parts pose estimation in robotics.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DecomPose introduces difficulty-aware gradient decoupling and asymmetric branching to reduce cross-category optimization contention in category-level 6D pose estimation, reporting better results on REAL275, CAMERA25, and HouseCat6D.
citing papers explorer
-
Generalizable and Actionable Parts Pose Estimation with Symmetry Annotation-Free Learning Strategy
SAFAG introduces a symmetry annotation-free two-stage learning strategy for generalizable actionable parts pose estimation in robotics.
-
DecomPose: Disentangling Cross-Category Optimization Contention for Category-Level 6D Object Pose Estimation
DecomPose introduces difficulty-aware gradient decoupling and asymmetric branching to reduce cross-category optimization contention in category-level 6D pose estimation, reporting better results on REAL275, CAMERA25, and HouseCat6D.