1. This article presents a new technique called RawNeRF, which uses Neural Radiance Fields (NeRF) to perform high quality novel view synthesis from noisy raw images.
2. RawNeRF is able to manipulate focus, exposure, and tonemapping after the fact, and is highly robust to the zero-mean distribution of raw noise.
3. When optimized over many noisy raw inputs (25-200), RawNeRF produces a scene representation so accurate that its rendered novel views outperform dedicated single and multi-image deep raw denoisers run on the same wide baseline input images.
The article “NeRF in the Dark: High Dynamic Range View Synthesis From Noisy Raw Images” provides an overview of a new technique called RawNeRF for performing high quality novel view synthesis from noisy raw images. The authors present their findings in a clear and concise manner, providing evidence for their claims with examples and experiments. The article does not appear to be biased or one-sided in any way, as it presents both sides of the argument equally and fairly. Furthermore, all claims are supported by evidence and there are no unsupported claims or missing points of consideration.
The only potential issue with this article is that it does not explore any counterarguments or alternative solutions to the problem presented. While this may be due to space constraints, it would have been beneficial if the authors had discussed some other possible approaches or solutions that could be used instead of RawNeRF. Additionally, there is no promotional content in this article; rather, it focuses solely on presenting the facts about RawNeRF without attempting to promote it in any way.
In conclusion, this article appears to be trustworthy and reliable overall; however, further exploration into alternative solutions would have been beneficial for readers who are looking for more information on this topic.