Information
- Publication Type: Conference Paper
- Workgroup(s)/Project(s):
- Date: May 2020
- Lecturer: Bernhard Kerbl
- Event: I3D ’20
- Call for Papers: Call for Paper
- Booktitle: Symposium on Interactive 3D Graphics and Games
- Pages: 1 – 9
Abstract
With the introduction of hardware-supported ray tracing and deep learning for denoising, computer graphics has made a considerable step toward real-time global illumination. In this work, we present an alternative global illumination method: The stochastic substitute tree (SST), a hierarchical structure inspired by lightcuts with light probability distributions as inner nodes. Our approach distributes virtual point lights (VPLs) in every frame and efficiently constructs the SST over those lights by clustering according to Morton codes. Global illumination is approximated by sampling the SST and considers the BRDF at the hit location as well as the SST nodes’ intensities for importance sampling directly from inner nodes of the tree. To remove the introduced Monte Carlo noise, we use a recurrent autoencoder. In combination with temporal filtering, we deliver real-time global illumination for complex scenes with challenging light distributions.Additional Files and Images
Weblinks
BibTeX
@inproceedings{tatzgern-2020-sst, title = "Stochastic Substitute Trees for Real-Time Global Illumination", author = "Wolfgang Tatzgern and Benedikt Mayr and Bernhard Kerbl and Markus Steinberger", year = "2020", abstract = "With the introduction of hardware-supported ray tracing and deep learning for denoising, computer graphics has made a considerable step toward real-time global illumination. In this work, we present an alternative global illumination method: The stochastic substitute tree (SST), a hierarchical structure inspired by lightcuts with light probability distributions as inner nodes. Our approach distributes virtual point lights (VPLs) in every frame and efficiently constructs the SST over those lights by clustering according to Morton codes. Global illumination is approximated by sampling the SST and considers the BRDF at the hit location as well as the SST nodes’ intensities for importance sampling directly from inner nodes of the tree. To remove the introduced Monte Carlo noise, we use a recurrent autoencoder. In combination with temporal filtering, we deliver real-time global illumination for complex scenes with challenging light distributions.", month = may, event = "I3D ’20", booktitle = "Symposium on Interactive 3D Graphics and Games", pages = "1--9", URL = "https://www.cg.tuwien.ac.at/research/publications/2020/tatzgern-2020-sst/", }