Currius, Roc RamonDolonius, DanAssarsson, UlfSintorn, ErikPanozzo, Daniele and Assarsson, Ulf2020-05-242020-05-2420201467-8659https://doi.org/10.1111/cgf.13918https://diglib.eg.org:443/handle/10.1111/cgf13918We describe a method to use Spherical Gaussians with free directions and arbitrary sharpness and amplitude to approximate the precomputed local light field for any point on a surface in a scene. This allows for a high-quality reconstruction of these light fields in a manner that can be used to render the surfaces with precomputed global illumination in real-time with very low cost both in memory and performance. We also extend this concept to represent the illumination-weighted environment visibility, allowing for high-quality reflections of the distant environment with both surface-material properties and visibility taken into account. We treat obtaining the Spherical Gaussians as an optimization problem for which we train a Convolutional Neural Network to produce appropriate values for each of the Spherical Gaussians' parameters. We define this CNN in such a way that the produced parameters can be interpolated between adjacent local light fields while keeping the illumination in the intermediate points coherentAttribution 4.0 International LicenseComputing methodologiesRenderingRay tracing10.1111/cgf.13918133-146