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About this paper
Qiujie Dong, Zixiong Wang, Manyi Li, Junjie Gao, Shuangmin Chen, Zhenyu Shu, Shiqing Xin, Changhe Tu and 1 more
Laplacian2Mesh proposes a deep learning framework that operates entirely in the Laplacian-Beltrami spectral domain for 3D mesh understanding. This approach achieves robust performance on shape classification and semantic segmentation tasks, notably demonstrating enhanced resistance to noise and various mesh defects compared to previous methods.
Key insights
- Low-frequency signals derived from the Laplacian-Beltrami operator encode the overall shape, which is semantically important for mesh understanding tasks.
- Spectral transformations inherently decouple shape variations from irregular mesh triangulation, enhancing robustness to mesh defects and noise.
- A multi-resolution U-Net inspired architecture, combined with novel Laplacian pooling and unpooling, effectively processes spectral features across different scales.
Summary by alphaXiv.
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