Symmetry Informative and Agnostic Feature Disentanglement for 3D Shapes

Shape descriptors, i.e. per-vertex features of 3D meshes or point clouds, are fundamental to shape analysis. Over the past decades, 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 a recent method successfully extracted symmetry-informative feature from semantic-aware descriptors, its features are only one-dimensional, neglecting other valuable semantic information. Besides, the extracted symmetry-informative feature is usually noisy and yields tiny miss-classified patches. To address these gaps, we propose a feature disentanglement approach which at the same time is symmetry informative and symmetry agnostic. Further, we propose a feature refinement technique to improve 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.