SAwareSSGI: Surrounding-Aware Screen-Space Global Illumination Using Generative Adversarial Networks

Abstract

Global Illumination (GI) is a technique that is employed in computer graphics to enhance realism. Various methods have been used to achieve this using computer-generated imagery. The most precise method involves conventional ray tracing, which yields highly realistic results but is computationally intensive and unsuitable for real-time applications. Alternatively, faster algorithms utilize post-processing on rasterization, making them more suitable for real-time scenarios. However, these algorithms are also resource-intensive and may produce inaccurate lighting due to limited information on screen-space features. our proposal involves utilizing a Generative Adversarial Network (GAN) approach to achieve real-time GI effects, following the methodology of conventional screen-space GI techniques. We take surrounding graphical information into account by going beyond screen-space and producing consistent GI effects that are comparatively closer to their physically correct ray-tracing counterpart. Moreover, our model provides a better quality of generated output than the other recent model which utilized a similar approach by scoring 0.90811 in SSIM, 0.00093 in MSE, and 30.30576 dB in PSNR on our developed dataset.

Keywords

• Computer graphics
• global illumination
• GAN
• neural networks