# Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware

arXiv: https://arxiv.org/abs/2110.12039 · HTML: https://mlsanity.com/paper/2110.12039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network](https://mlsanity.com/paper/1710.09834/) (2017)

## Abstract (arXiv)

We give an overview of the different rendering methods and we demonstrate that the use of a Generative Adversarial Networks (GAN) for Global Illumination (GI) gives a superior quality rendered image to that of a rasterisations image. We utilise the Pix2Pix architecture and specify the hyper-parameters and methodology used to mimic ray-traced images from a set of input features. We also demonstrate that the GANs quality is comparable to the quality of the ray-traced images, but is able to produce the image, at a fraction of the time.