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- W3208483980 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" @default.
- W3208483980 created "2021-11-08" @default.
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- W3208483980 date "2022-03-01" @default.
- W3208483980 modified "2023-09-27" @default.
- W3208483980 title "Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware" @default.
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- W3208483980 doi "https://doi.org/10.18178/ijeee.10.1.1-6" @default.
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