Importance Sampling of Many Lights with Reinforcement Lightcuts Learning

In this manuscript, we introduce a novel technique for sampling and integrating direct illumination in the presence of many lights. Unlike previous work, the presented technique importance samples the product distribution of radiance and visibility while using bounded memory footprint and very low s...

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Bibliographic Details
Main Author Pantaleoni, Jacopo
Format Journal Article
LanguageEnglish
Published 22.11.2019
Subjects
Computer Science - Data Structures and Algorithms
Computer Science - Graphics
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Summary:In this manuscript, we introduce a novel technique for sampling and integrating direct illumination in the presence of many lights. Unlike previous work, the presented technique importance samples the product distribution of radiance and visibility while using bounded memory footprint and very low sampling overhead. This is achieved by learning a compact approximation of the target distributions over both space and time, allowing to reuse and adapt the learnt distributions both spatially, within a frame, and temporally, across multiple frames. Finally, the technique is amenable to massive parallelization on GPUs and suitable for both offline and real-time rendering.
DOI:10.48550/arxiv.1911.10217