Robust Symmetry Detection via Riemannian Langevin Dynamics

TL;DR

This paper introduces a robust symmetry detection method that combines classical geometry techniques with Langevin dynamics in a learned symmetry space, effectively handling noise and partial symmetries in 3D shapes.

Contribution

It proposes a novel approach that integrates Langevin dynamics with symmetry detection, improving robustness to noise and partial symmetries over existing methods.

Findings

Effective detection of partial and global symmetries in noisy shapes

Enhanced robustness to noise demonstrated on various shape datasets

Utility in downstream tasks like shape compression and symmetrization

Abstract

Symmetries are ubiquitous across all kinds of objects, whether in nature or in man-made creations. While these symmetries may seem intuitive to the human eye, detecting them with a machine is nontrivial due to the vast search space. Classical geometry-based methods work by aggregating "votes" for each symmetry but struggle with noise. In contrast, learning-based methods may be more robust to noise, but often overlook partial symmetries due to the scarcity of annotated data. In this work, we address this challenge by proposing a novel symmetry detection method that marries classical symmetry detection techniques with recent advances in generative modeling. Specifically, we apply Langevin dynamics to a redefined symmetry space to enhance robustness against noise. We provide empirical results on a variety of shapes that suggest our method is not only robust to noise, but can also identify both partial and global symmetries. Moreover, we demonstrate the utility of our detected symmetries in various downstream tasks, such as compression and symmetrization of noisy shapes.