Please use this identifier to cite or link to this item: doi:10.22028/D291-39185
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Title: Variance-aware path guiding
Author(s): Rath, Alexander
Grittmann, Pascal
Herholz, Sebastian
Vévoda, Petr
Slusallek, Philipp
Křivánek, Jaroslav
Language: English
Title: ACM transactions on graphics : TOG
Volume: 39
Issue: 4
Publisher/Platform: ACM
Year of Publication: 2020
DDC notations: 004 Computer science, internet
Publikation type: Journal Article
Abstract: Path guiding is a promising tool to improve the performance of path tracing algorithms. However, not much research has investigated what target densities a guiding method should strive to learn for optimal performance. Instead, most previous work pursues the zero-variance goal: The local decisions are guided under the assumption that all other decisions along the random walk will be sampled perfectly. In practice, however, many decisions are poorly guided, or not guided at all. Furthermore, learned distributions are often marginalized, e.g., by neglecting the BSDF. We present a generic procedure to derive theoretically optimal target densities for local path guiding. These densities account for variance in nested estimators, and marginalize provably well over, e.g., the BSDF. We apply our theory in two state-of-the-art rendering applications: a path guiding solution for unidirectional path tracing [Müller et al. 2017] and a guiding method for light source selection for the many lights problem [Vévoda et al. 2018]. In both cases, we observe significant improvements, especially on glossy surfaces. The implementations for both applications consist of trivial modifications to the original code base, without introducing any additional overhead.
DOI of the first publication: 10.1145/3386569.3392441
URL of the first publication:
Link to this record: urn:nbn:de:bsz:291--ds-391855
ISSN: 1557-7368
Date of registration: 1-Mar-2023
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Philipp Slusallek
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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