Shape descriptors, i.e., per-vertex features of 3D meshes or point clouds, are fundamental to shape analysis. Historically, various handcrafted geometry-aware descriptors and feature refinement techniques have been proposed. Recently, several studies have initiated a new research direction by leveraging features from image foundation models to create semantics-aware descriptors, demonstrating advantages across tasks like shape matching, editing, and segmentation. Symmetry, another key concept in shape analysis, has also attracted increasing attention. Consequently, constructing symmetry-aware shape descriptors is a natural progression. Although the recent method $χ$ (Wang et al., 2025) successfully extracted symmetry-informative features from semantic-aware descriptors, its features are only one-dimensional, neglecting other valuable semantic information. Furthermore, the extracted symmetry-informative feature is usually noisy and yields small misclassified patches. To address these gaps, we propose a feature disentanglement approach which is simultaneously symmetry informative and symmetry agnostic. Further, we propose a feature refinement technique to improve the robustness of predicted symmetry informative features. Extensive experiments, including intrinsic symmetry detection, left/right classification, and shape matching, demonstrate the effectiveness of our proposed framework compared to various state-of-the-art methods, both qualitatively and quantitatively.

翻译：形状描述符，即三维网格或点云中每个顶点的特征，是形状分析的基础。历史上，人们提出了各种手工设计的几何感知描述符和特征精炼技术。最近，一些研究开创了新的研究方向，通过利用图像基础模型的特征来创建语义感知描述符，并在形状匹配、编辑和分割等任务中展现出优势。对称性作为形状分析中的另一个关键概念，也日益受到关注。因此，构建对称感知的形状描述符是自然的发展方向。尽管近期方法$χ$（Wang等人，2025年）成功地从语义感知描述符中提取了对称信息性特征，但其特征仅为一维，忽略了其他有价值的语义信息。此外，提取的对称信息性特征通常含有噪声，并会产生小的误分类区域。为弥补这些不足，我们提出了一种同时具备对称信息性与对称无关性的特征解耦方法。进一步，我们提出了一种特征精炼技术，以提升预测的对称信息性特征的鲁棒性。大量实验，包括内蕴对称性检测、左右分类以及形状匹配，从定性和定量两方面证明了我们提出的框架相较于多种先进方法的有效性。