Summary of Robust Symmetry Detection Via Riemannian Langevin Dynamics, by Jihyeon Je et al.
Robust Symmetry Detection via Riemannian Langevin Dynamics
by Jihyeon Je, Jiayi Liu, Guandao Yang, Boyang Deng, Shengqu Cai, Gordon Wetzstein, Or Litany, Leonidas Guibas
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed symmetry detection method combines classical geometry-based techniques with generative modeling to detect symmetries in various shapes. The novel approach uses Langevin dynamics to redefined symmetry spaces, enhancing robustness against noise. Experimental results on different shapes show the method’s ability to identify both partial and global symmetries, as well as its utility in compression and symmetrization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to find symmetries in objects using machine learning. It combines old geometry-based methods with new generative modeling techniques to make it more robust against noise. The method can detect both partial and full symmetries, and it’s useful for tasks like compressing and cleaning up noisy shapes. |
Keywords
» Artificial intelligence » Machine learning
