Sequential monte carlo integration in low-anisotropy participating media

  • Authors:
  • Vincent Pegoraro;Ingo Wald;Steven G. Parker

  • Affiliations:
  • University of Utah;University of Utah and Intel;University of Utah and NVIDIA

  • Venue:
  • EGSR'08 Proceedings of the Nineteenth Eurographics conference on Rendering
  • Year:
  • 2008

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Abstract

This paper presents a novel method that effectively combines both control variates and importance sampling in a sequential Monte Carlo context. The radiance estimates computed during the rendering process are cached in a 5D adaptive hierarchical structure that defines dynamic predicate functions for both variance reduction techniques and guarantees well-behaved PDFs, yielding continually increasing efficiencies thanks to a marginal computational overhead. While remaining unbiased, the technique is effective within a single pass as both estimation and caching are done online, exploiting the coherency in illumination while being independent of the actual scene rep- resentation. The method is relatively easy to implement and to tune via a single parameter, and we demonstrate its practical benefits with important gains in convergence rate and competitive results with state of the art techniques.