发表机构
Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出全自动无参考的3D旋转对称分类与轴定位方法，通过自一致性分析和层级公式重建对称结构，在GSO上达94.75%准确率，并提升下游6D姿态估计精度。
AI 中文摘要
旋转对称性是6D姿态估计中的一个重要先验，能够提高姿态精度并支持对称性感知评估。然而，当前3D物体的对称性标注大多依赖人工或半自动方式，通常需要预定义类型或阶数，这限制了可扩展性。本工作提出了一种全自动、无参考框架的方法，用于对称类型分类、旋转阶数识别以及全部八种规范3D旋转对称类型的完整轴定位。该方法定位一个主导的高阶轴，通过自一致性分析推断其旋转阶数，并在层级引导公式下重建完整的对称结构。一种纹理感知扩展进一步建模了由外观引起的旋转阶数降低，同时保持轴方向。在理想化和真实世界数据集上的实验证明了强大的准确性和泛化能力，在GSO的438个对称物体上达到94.75%的准确率。使用这些先验训练FoundationPose，在五个BOP数据集上将精度提高了最多0.9%，表明自动估计的旋转先验改善了下游6D姿态估计。代码可在该https URL获取。
英文摘要
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.
CommentsCVPR 2026. 10 pages, 4 figures. Mengxin Zhang and Yulin Wang contributed equally. Corresponding authors: Chen Luo and Yijun Zhou
Journal refProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 13866-13875
