Summary of Refine: Recursive Field Networks For Cross-modal Multi-scene Representation, by Sergey Zakharov et al.
ReFiNe: Recursive Field Networks for Cross-modal Multi-scene Representation
by Sergey Zakharov, Katherine Liu, Adrien Gaidon, Rares Ambrus
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 This paper presents a new approach to encoding multiple shapes in a single model, achieving high precision while minimizing memory usage. By exploiting object self-similarity through a recursive hierarchical formulation, the method enables efficient shape latent spaces without requiring auxiliary data structures. The proposed technique supports spatial and global-to-local feature fusion, allowing for continuous field queries and applications such as raytracing. Experiments on diverse datasets demonstrate state-of-the-art multi-scene reconstruction and compression results with a single network per dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to store many shapes in one place without using too much memory or computer power. The researchers found a way to make the shapes look similar, which makes it more efficient. This new approach allows us to do things like create realistic images and videos by combining multiple shapes. The results are impressive and show that this method is better than others at reconstructing and compressing many scenes with just one network for each scene. |
Keywords
» Artificial intelligence » Precision
