Deep radiance caching: Convolutional autoencoders deeper in ray tracing
File(s)
Accepted version
Author(s)
Giulio, Jiang
Kainz, Bernhard
Type
Journal Article
Abstract
Rendering realistic images with global illumination is a computationally demanding task and often requires dedicated hardware for feasible runtime. Recent research uses Deep Neural Networks to predict indirect lighting on image level, but such methods are commonly limited to diffuse materials and require training on each scene. We present Deep Radiance Caching (DRC), an efficient variant of Radiance Caching utilizing Convolutional Autoencoders for rendering global illumination. DRC employs a denoising neural network with Radiance Caching to support a wide range of material types, without the requirement of offline pre-computation or training for each scene. This offers high performance CPU rendering for maximum accessibility. Our method has been evaluated on interior scenes, and is able to produce high-quality images within 180 s on a single CPU.
Date Issued
2021-02-01
Date Acceptance
2020-09-26
Citation
Computers and Graphics (UK), 2021, 94, pp.22-31
ISSN
0097-8493
Publisher
Elsevier
Start Page
22
End Page
31
Journal / Book Title
Computers and Graphics (UK)
Volume
94
Copyright Statement
© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Nvidia
Grant Number
Nvidia Hardware donation
Subjects
cs.GR
cs.GR
Software Engineering
0801 Artificial Intelligence and Image Processing
0803 Computer Software
Publication Status
Published
Date Publish Online
2020-10-07