让3D形状识别左右对称性，提升点云与网格分析能力
Symmetry Understanding of 3D Shapes via Chirality Disentanglement
- 基于2D视觉模型无监督提取形状的左右特征
- 在多个数据集上实现左/右对称部分有效区分
- 适合需要对称性理解的3D形状分析任务
手性信息（即区分左右的能力）在计算机视觉的各类数据中普遍存在，包括图像、视频、点云和网格。尽管手性在图像领域已有广泛研究，但在形状分析（如点云与网格）中的探索仍不充分。现有形状顶点描述子虽对刚体变换鲁棒，却常无法区分左右对称部分。鉴于手性在多种形状分析问题中的普遍性及当前描述子缺乏手性感知，构建手性特征提取器成为迫切需求。本文基于Diff3F框架，提出一种无监督的手性特征提取流程，从2D基础模型中提取手性信息并赋予形状顶点。在多个数据集上通过定量与定性实验验证了所提特征的有效性。下游任务如左右分离、形状匹配与部件分割均表现出显著性能提升，证明其实用价值。
原文摘要 · Abstract (English)
Chirality information (i.e. information that allows distinguishing left from right) is ubiquitous for various data modes in computer vision, including images, videos, point clouds, and meshes. While chirality has been extensively studied in the image domain, its exploration in shape analysis (such as point clouds and meshes) remains underdeveloped. Although many shape vertex descriptors have shown appealing properties (e.g. robustness to rigid-body transformations), they are often not able to disambiguate between left and right symmetric parts. Considering the ubiquity of chirality information in different shape analysis problems and the lack of chirality-aware features within current shape descriptors, developing a chirality feature extractor becomes necessary and urgent. Based on the recent Diff3F framework, we propose an unsupervised chirality feature extraction pipeline to decorate shape vertices with chirality-aware information, extracted from 2D foundation models. We evaluated the extracted chirality features through quantitative and qualitative experiments across diverse datasets. Results from downstream tasks including left-right disentanglement, shape matching, and part segmentation demonstrate their effectiveness and practical utility. Project page: https://wei-kang-wang.github.io/chirality/
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