Jaroslav Křivánek

Progressive Light Transport Simulation on the GPU: Survey and Improvements

Scenes used to perform our experiments.

Abstract

Graphics Processing Units (GPUs) recently became general enough to enable implementation of a variety of light transport algorithms. However, the efficiency of these GPU implementations has received relatively little attention in the research literature and no systematic study on the topic exists to date. The goal of our work is to fill this gap. Our main contribution is a comprehensive and in-depth investigation of the efficiency of the GPU implementation of a number of classic as well as more recent progressive light transport simulation algorithms. We present several improvements over the state-of-the-art. In particular, our Light Vertex Cache, a new approach to mapping connections of sub-path vertices in Bidirectional Path Tracing on the GPU, outperforms the existing implementations by 30-60%. We also describe a first GPU implementation of the recently introduced Vertex Connection and Merging algorithm [Georgiev et al. 2012], showing that even relatively complex light transport algorithms can be efficiently mapped on the GPU. With the implementation of many of the state-of-the-art algorithms within a single system at our disposal, we present a unique direct comparison and analysis of their relative performance.

Publication

Tomáš Davidovič, Jaroslav Křivánek, Miloš Hašan, and Philipp Slusallek. Progressive Light Transport Simulation on the GPU: Survey and Improvements. ACM Trans. Graph. (2014) ... DOI | BibTeX

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Acknowledgments

We would like to thank the following people for providing the respective scenes: Ludvík Koutný for CoronaRoom, Jerome White for CoronaWatch, Iliyan Georgiev for LivingRoom, Jiří “Biolit” Friml for BiolitFull, Marko Dabrovic and Frank Meinl from CryTEK for CrytekSponza, and the Cornell University Program of Computer Graphics for GrandCentral. We would also like to thank Jan Novák and Iliyan Georgiev for their consultations on many finer implementation details. Thanks to Petr Kadleček and Martin Kahoun for proofreading. This work was supported by the Czech Science Foundation grant P202-13-26189S.