Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware
This enables faster rendering for applications on older GPU hardware, but is incremental as it adapts existing GAN methods to a specific domain.
The paper tackles the problem of slow ray-traced global illumination by using a Generative Adversarial Network (GAN) to mimic ray-traced images, achieving comparable quality at a fraction of the time.
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.