Summary of Mdnf: Multi-diffusion-nets For Neural Fields on Meshes, by Avigail Cohen Rimon et al.
MDNF: Multi-Diffusion-Nets for Neural Fields on Meshes
by Avigail Cohen Rimon, Tal Shnitzer, Mirela Ben Chen
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 A novel framework for representing neural fields on triangle meshes is proposed, offering a multi-resolution approach across both spatial and frequency domains. Inspired by the Neural Fourier Filter Bank (NFFB), the architecture decomposes these domains by associating finer spatial resolutions with higher frequencies and coarser resolutions with lower frequencies. Multiple DiffusionNet components are used to achieve geometry-aware spatial decomposition, followed by Fourier feature mapping to encourage finer resolution levels to be associated with higher frequencies. The final signal is composed using a sine-activated MLP, aggregating higher-frequency signals on top of lower-frequency ones. This architecture attains high accuracy in learning complex neural fields and is robust to various challenges. It is demonstrated through applications to diverse neural fields, including synthetic RGB functions, UV texture coordinates, and vertex normals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to represent neural fields on triangle meshes that works well across different scales. It’s like having a special filter that can pick out specific details or patterns in the data, depending on how big or small you want to look at it. The method uses multiple pieces of information from the data to figure out what the neural field should look like, and then combines those pieces together to get a final answer. This makes it good at learning complex patterns and robust to changes in the data. |
