Summary of Learning Novel View Synthesis From Heterogeneous Low-light Captures, by Quan Zheng et al.
Learning Novel View Synthesis from Heterogeneous Low-light Captures
by Quan Zheng, Hao Sun, Huiyao Xu, Fanjiang Xu
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 focuses on improving neural radiance fields (NRFs) for novel view synthesis. Current NRFs excel when input views have the same brightness level under fixed normal lighting. However, they struggle with heterogeneous brightness levels captured under low-light conditions, resulting in low-contrast images and degraded image quality. To address this challenge, the authors propose decomposing illumination, reflectance, and noise from input views, assuming reflectance remains invariant across different views. They learn an illumination embedding and optimize a noise map for each view to handle heterogeneous brightness and noise levels. An illumination adjustment module is designed for intuitive editing of the illumination component. The proposed approach achieves superior performance in synthesizing novel views compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a photo, but it’s too dark or noisy. This paper tries to fix that problem. Currently, machines are good at making new pictures from similar ones taken in the same light. But what if you took those photos in different lighting conditions? It gets tricky! The authors want to make it work by breaking down the picture into its parts: how bright it is, what’s reflected, and any noise. They learn to handle different levels of brightness and noise for each picture. With this new approach, they can even adjust the brightness of a picture after taking it. This helps machines make better new pictures from old ones, especially in low-light conditions. |
Keywords
» Artificial intelligence » Embedding
