I have seen a new paper released by Google Research Team in Neural Radiance Field (NeRF) area and it was a marvelous improvement 😱 in NeRF field. Let’s see what is it.
A neural radiance field (NeRF) is a fully-connected neural network that can generate novel views of complex 3D scenes, based on a partial set of 2D images. It is trained to use a rendering loss to reproduce input views of a scene. It works by taking input images representing a scene and interpolating between them to render one complete scene. NeRF is a highly effective way to generate images for synthetic data.
You can watch the above video to see an overview of the neural radiance field (NeRF) paper. Also can refer following links for more information about NeRF.
🔗Web Site: https://www.matthewtancik.com/nerf
🔗Paper: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis — https://arxiv.org/abs/2003.08934
This is the improved method of NeRF by Google’s research team. Now RawNeRF can see dark images clearly and can render them. The paper abstraction can see in below.
Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis from a collection of posed input images. Like most view synthesis methods, NeRF uses tonemapped low dynamic range (LDR) as input; these images have been processed by a lossy camera pipeline that smooths detail, clips highlights, and distorts the simple noise distribution of raw sensor data. We modify NeRF to instead train directly on linear raw images, preserving the scene’s full dynamic range. By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks. In addition to changing the camera viewpoint, we can manipulate focus, exposure, and tonemapping after the fact. Although a single raw image appears significantly more noisy than a postprocessed one, we show that NeRF is highly robust to the zero-mean distribution of raw noise. When optimized over many noisy raw inputs (25–200), NeRF 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. As a result, our method, which we call RawNeRF, can reconstruct scenes from extremely noisy images captured in near-darkness.
The below video show an overview of the new RawNeRF paper.
🔗Paper: NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images — https://arxiv.org/abs/2111.13679
You can watch the below video for a good explanation of the new RawNeRF paper create on Two Minute Papers youtube channel.
✅👉What do you think about the new improvement of NeRF? 💭Comment below.
✅👉If you like this article, 👏clap to this article.
✅👉If you like my articles or need to read new articles, please ➕ follow me.
✌️What do you need next? Suggest titles for 📃 new articles.
🙏Thank you for reading my articles.