Spherical Gaussian Light‐field Textures for Fast Precomputed Global Illumination
Artikel i vetenskaplig tidskrift, 2020

We 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 coherent.

Ray tracing

• Computing methodologies → Rendering

CCS Concepts


Roc Ramon Currius

Chalmers, Data- och informationsteknik, Datorteknik

Dan Dolonius

Chalmers, Data- och informationsteknik, Datorteknik

Ulf Assarsson

Chalmers, Data- och informationsteknik, Datorteknik

Erik Sintorn

Chalmers, Data- och informationsteknik, Datorteknik

Computer Graphics Forum

0167-7055 (ISSN) 1467-8659 (eISSN)

Vol. 39 2 133-146

Komprimering av förberäknad belysningsinformation

Vetenskapsrådet (VR) (2017-05060), 2018-01-01 -- 2021-12-31.



Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)


Hållbar utveckling



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