[
    {
        "id": "osp-15354",
        "type": "article-journal",
        "title": "KASALv2: Fully Automatic 3D Rotational Symmetry Classification and Axis Localization",
        "author": [
            {
                "family": "Zhang",
                "given": "Mengxin"
            },
            {
                "family": "Wang",
                "given": "Yulin"
            },
            {
                "family": "Luo",
                "given": "Chen"
            },
            {
                "family": "Li",
                "given": "Yongzhe"
            },
            {
                "family": "Zhou",
                "given": "Yijun"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/kasalv2-fully-automatic-3d-rotational-symmetry-classification-and-axis-localization",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]