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

Jared Harris-Dewey; Richard Klein

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arXiv · 2110.12039

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

Jared Harris-Dewey·Richard Klein

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Abstract

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.

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BibTeX RIS

Jared Harris-Dewey, Richard Klein. 2021-10-22. Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware. https://arxiv.org/abs/2110.12039

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