Summary of Neural Nerf Compression, by Tuan Pham et al.
Neural NeRF Compression
by Tuan Pham, Stephan Mandt
First submitted to arxiv on: 13 Jun 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 This paper presents a novel approach to efficiently compressing Neural Radiance Fields (NeRFs) for capturing detailed 3D scenes. Recent NeRFs utilize feature grids, which introduce significant storage overhead. Our method employs neural compression to compress the model’s feature grids, addressing this concern. We design an encoder-free, end-to-end optimized approach using lightweight decoders and leverage spatial inhomogeneity of latent feature grids with importance-weighted rate-distortion objectives and sparse entropy models. Experimental results show that our proposed method surpasses existing works in terms of grid-based NeRF compression efficacy and reconstruction quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in computer vision called Neural Radiance Fields (NeRFs). NeRFs help us create detailed 3D scenes, but they take up too much space. The new method makes these scenes more efficient to store without losing any details. It does this by using special codes and algorithms that work well with the way data is stored in these scenes. We tested our method and it works better than previous methods. |
Keywords
» Artificial intelligence » Encoder
