Symmetry Understanding of 3D Shapes via Chirality Disentanglement

TL;DR

This paper introduces an unsupervised method to extract chirality-aware features from 3D shapes, enhancing shape analysis tasks like left-right disentanglement and shape matching by leveraging 2D foundation models.

Contribution

It presents the first unsupervised chirality feature extractor for 3D shapes, improving shape analysis by incorporating chirality information into shape descriptors.

Findings

Chirality features improve left-right shape disentanglement.

Enhanced shape matching accuracy with chirality-aware features.

Effective part segmentation using chirality information.

Abstract

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/