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Importance Sampling of Many Lights with Adaptive Tree Splitting

Published:24 August 2018Publication History
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Abstract

We present a technique to importance sample large collections of lights (including mesh lights as collections of small emitters) in the context of Monte-Carlo path tracing. A bounding volume hierarchy over all emitters is traversed at each shading point using a single random number in a way that importance samples their predicted contribution. The tree aggregates energy, spatial and orientation information from the emitters to enable accurate prediction of the effect of a cluster of lights on any given shading point. We further improve the performance of the algorithm by forcing splitting until the importance of a cluster is sufficiently representative of its contents.

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      cover image Proceedings of the ACM on Computer Graphics and Interactive Techniques
      Proceedings of the ACM on Computer Graphics and Interactive Techniques  Volume 1, Issue 2
      August 2018
      223 pages
      EISSN:2577-6193
      DOI:10.1145/3273023
      Issue’s Table of Contents

      Copyright © 2018 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 24 August 2018
      Published in pacmcgit Volume 1, Issue 2

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