Sequential Monte Carlo Adaptation in Low-Anisotropy Participating Media

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 representation. 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.