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Selective guided sampling with complete light transport paths

Simon, Florian; Jung, Alisa 1; Hanika, Johannes 1; Dachsbacher, Carsten 1
1 Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Finding good global importance sampling strategies for Monte Carlo light transport is challenging. While estimators using local methods (such as BSDF sampling or next event estimation) often work well in the majority of a scene, small regions in path space can be sampled insufficiently (e.g. a reflected caustic). We propose a novel data-driven guided sampling method which selectively adapts to such problematic regions and complements the unguided estimator. It is based on complete transport paths, i.e. is able to resolve the correlation due to BSDFs and free flight distances in participating media. It is conceptuall simple and places anisotropic truncated Gaussian distributions around guide paths to reconstruct a continuous probability density function (guided PDF). Guide paths are iteratively sampled from the guided as well as the unguided PDF and only recorded if they cause high variance in the current estimator. While plain Monte Carlo samples paths independently and Markov chain-based methods perturb a single current sample, we determine the reconstruction kernels by a set of neighbouring paths. This enables local exploration of the integrand without detailed balance constraints or the need for analytic derivatives. ... mehr

DOI: 10.1145/3272127.3275030
Zitationen: 11
Web of Science
Zitationen: 20
Zitationen: 27
Zugehörige Institution(en) am KIT Institut für Visualisierung und Datenanalyse (IVD)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 0730-0301, 1557-7368
KITopen-ID: 1000120194
Erschienen in ACM transactions on graphics
Verlag Association for Computing Machinery (ACM)
Band 37
Heft 6
Seiten Article no: 223
Schlagwörter Rendering, Path Tracing, Path Guiding, Guiding, Monte Carlo
Nachgewiesen in Web of Science
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