Stochastic Lightcuts

Yuksel, Cem

Stochastic Lightcuts

Files

027-032.pdf (28.33 MB)

stochastic_lightcuts-supplemental.pdf (15.26 MB)

slc.mp4 (201.02 MB)

Date

2019

Authors

Yuksel, Cem

Publisher

The Eurographics Association

Abstract

We introduce stochastic lightcuts by combining the lighting approximation of lightcuts with stochastic sampling for efficiently rendering scenes with a large number of light sources. Our stochastic lightcuts method entirely eliminates the sampling correlation of lightcuts and replaces it with noise. To minimize this noise, we present a robust hierarchical sampling strategy, combining the benefits of importance sampling, adaptive sampling, and stratified sampling. Our approach also provides temporally stable results and lifts any restrictions on the light types that can be approximated with lightcuts. We present examples of using stochastic lightcuts with path tracing as well as indirect illumination with virtual lights, achieving more than an order of magnitude faster render times than lightcuts by effectively approximating direct illumination using a small number of light samples, in addition to providing temporal stability. Our comparisons to other stochastic sampling techniques demonstrate that we provide superior sampling quality that matches and improves the excellent convergence rates of the lightcuts approach.

        @inproceedings{10.2312:hpg.20191192
,
booktitle = {High-Performance Graphics - Short Papers
},
editor = {Steinberger, Markus and Foley, Tim
},
title = {{Stochastic Lightcuts
}},
author = {Yuksel, Cem
},
year = {2019
},
publisher = {The Eurographics Association
},
ISSN = {2079-8687
},
ISBN = {978-3-03868-092-5
},
DOI = {10.2312/hpg.20191192
}
}