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

2026-10-07

AI Summary

This study addresses the reliance on manual annotation and limited scalability of existing 3D rotational symmetry labeling by proposing a fully automated, reference-free analytical framework. Methodologically, it introduces a novel hierarchy-guided symmetric structure reconstruction strategy that enables classification across eight symmetry types and full-axis localization. Furthermore, a texture-aware mechanism is incorporated to mitigate order degradation caused by object appearance, combined with self-consistency analysis to infer rotational orders. Experiments demonstrate that the proposed framework achieves 94.75% accuracy on the GSO dataset. Additionally, integrating the derived symmetry priors into FoundationPose yields accuracy improvements of up to 0.9% across five BOP benchmarks, validating its practical utility in 6D pose estimation tasks.

Abstract

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. 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. 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. 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 0.9% across five BOP datasets, showing that automatically estimated rotational priors improve downstream 6D pose estimation. Code is available at https://github.com/WangYuLin-SEU/KASAL.

Problem

• 3D rotational symmetry
• symmetry classification
• axis localization
• 6D pose estimation
• automatic annotation

Innovation

• 3D Rotational Symmetry
• Axis Localization
• Fully Automatic Framework
• Texture-aware Extension
• 6D Pose Estimation