Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware
Jared Harris-Dewey and Richard Klein
School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa
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. Source Code: https://github.com/Jaredrhd/Global-Illumination-using-Pix2Pix-GAN
Index Terms—Generative Adversarial Networks, Global Illumination, Indirect Lighting, Ray-tracing, Rendering, Machine Learning
Cite: Jared Harris-Dewey and Richard Klein, "Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware," International Journal of Electronics and Electrical Engineering, Vol. 10, No. 1, pp. 1-6, March 2022. doi: 10.18178/ijeee.10.1.1-6
Index Terms—Generative Adversarial Networks, Global Illumination, Indirect Lighting, Ray-tracing, Rendering, Machine Learning
Cite: Jared Harris-Dewey and Richard Klein, "Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware," International Journal of Electronics and Electrical Engineering, Vol. 10, No. 1, pp. 1-6, March 2022. doi: 10.18178/ijeee.10.1.1-6
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