KASALv2: Fully Automatic 3D Rotational Symmetry Classification and Axis Localization
This work introduces a fully automatic, reference-free framework for symmetry-type classification, rotational-order identification, and full-axis localization across all eight canonical 3D rotational symmetry types.
ProofPaper ↗
Key points
- Rotational symmetry is an important prior in 6D pose estimation, improving pose accuracy and supporting symmetry-aware evaluation.
- However, current symmetry annotations for 3D objects remain largely manual or semi-automatic, often requiring predefined types or orders, which limits scalability.
- The method localizes a dominant high-order axis, infers its rotational order through self-consistency analysis, and reconstructs the complete symmetry structure under a hierarchy-guided formulation.
- A texture-aware extension further models appearance-induced reductions in rotational order while preserving axis orientations.
Sources (1)
- [1]KASALv2: Fully Automatic 3D Rotational Symmetry Classification and Axis LocalizationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 06:37 AM
This work introduces a fully automatic, reference-free framework for symmetry-type classification, rotational-order identification, and full-axis localization across all eight canonical 3D rotational symmetry types.
Rotational symmetry is an important prior in 6D pose estimation, improving pose accuracy and supporting symmetry-aware evaluation.
Extractive summary: sentences quoted from the sources.