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On Learning the Best Balancing Strategy

David Murray 1 Sofiane Benzait 1 Romain Pacanowski 2 Xavier Granier 2, 3
1 MANAO - Melting the frontiers between Light, Shape and Matter
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, LP2N - Laboratoire Photonique, Numérique et Nanosciences
Abstract : Fast computation of light propagation using Monte Carlo techniques requires finding the best samples from the space of light paths. For the last 30 years, numerous strategies have been developed to address this problem but choosing the best one is really scene-dependent. Multiple Importance Sampling (MIS) emerges as a potential generic solution by combining different weighted strategies, to take advantage of the best ones. Most recent work have focused on defining the best weighting scheme. Among them, two paper have shown that it is possible, in the context of direct illumination, to estimate the best way to balance the number of samples between two strategies, on a per-pixel basis. In this paper, we extend this previous approach to Global Illumination and to three strategies.
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https://hal.inria.fr/hal-02494950
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Submitted on : Wednesday, May 6, 2020 - 6:28:58 PM
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David Murray, Sofiane Benzait, Romain Pacanowski, Xavier Granier. On Learning the Best Balancing Strategy. Eurographics 2020, May 2020, Norrköping, Sweden. pp.1 - 4, ⟨10.2312/egs.20201009⟩. ⟨hal-02494950v2⟩

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