Summary of Incremental Multi-scene Modeling Via Continual Neural Graphics Primitives, by Prajwal Singh et al.
Incremental Multi-Scene Modeling via Continual Neural Graphics Primitives
by Prajwal Singh, Ashish Tiwari, Gautam Vashishtha, Shanmuganathan Raman
First submitted to arxiv on: 29 Nov 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 The paper introduces Continual-Neural Graphics Primitives (C-NGP), a framework for incrementally encoding multiple 3D scenes into a single neural radiance field. This addresses scalability challenges faced by Neural Radiance Fields (NeRF) when rendering novel views for 3D scenes. C-NGP uses a generative replay approach, adapting to new scenes without requiring access to old data. The framework produces high-quality novel-view renderings on synthetic and real datasets, including the Real-LLFF dataset where it models all 8 scenes together with only a 2.2% drop in PSNR compared to vanilla NeRF. C-NGP also enables multiple style edits within the same network. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary C-NeRF is a new way to make pictures of things from different angles without having to take a lot of pictures first. Right now, we can only make these pictures for one thing at a time, but what if we could do it for many things all at once? That’s what the C-NGP does – it makes a special kind of picture that can be used for lots of different things, and it gets better with each new thing it sees. This is important because it could help us make movies or video games where the characters look more realistic. |
