Generative Adversarial Networks for Global Illumination and Indirect Lighting as a Replacement for Ray-tracing in Older GPU Hardware


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.

International Conference in Soft Computing and Machine Intelligence
Richard Klein
Richard Klein
PRIME Lab Director

I am an Associate Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand in Johannesburg, and a co-PI of the PRIME lab.