KASALv2: Fully Automatic 3D Rotational Symmetry Classification and Axis Localization

Rotational symmetry is an important prior in 6D pose estimation, improving pose accuracy and ensuring the consistency of symmetry-aware evaluation metrics. However, current symmetry annotations for 3D objects are still largely manual or semi-automatic, often requiring predefined symmetry types or rotational orders and thus limiting scalability. This work introduces a fully automatic and reference-free framework that performs symmetry-type classification, rotational-order identification, and full-axis localization across all eight canonical 3D rotational symmetry types. 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 geometric formulation. A texture-aware extension further models appearance-induced reductions in rotational order while preserving axis orientations. Extensive experiments on idealized and real-world datasets demonstrate strong accuracy and generalization, achieving 94.75% accuracy on 438 symmetric objects in GSO. Training FoundationPose with these priors improves accuracy by up to 1.0% across five BOP datasets, indicating that automatically estimated rotational priors can provide quantitative gains in downstream 6D pose estimation.