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Correlation‐Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms

Title: Correlation‐Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms
Authors: Grittmann, Pascal; Georgiev, Iliyan; Slusallek, Philipp
Publisher Information: Wiley
Publication Year: 2021
Collection: SciDok - Der Wissenschaftsserver der UdS (Universität des Saarlandes)
Subject Terms: ddc:004
Description: Combining diverse sampling techniques via multiple importance sampling (MIS) is key to achieving robustness in modern Monte Carlo light transport simulation. Many such methods additionally employ correlated path sampling to boost efficiency. Photon mapping, bidirectional path tracing, and path-reuse algorithms construct sets of paths that share a common prefix. This correlation is ignored by classical MIS heuristics, which can result in poor technique combination and noisy images. We propose a practical and robust solution to that problem. Our idea is to incorporate correlation knowledge into the balance heuristic, based on known path densities that are already required for MIS. This correlation-aware heuristic can achieve considerably lower error than the balance heuristic, while avoiding computational and memory overhead.
Document Type: article in journal/newspaper
Language: English
Relation: hdl:20.500.11880/35318
DOI: 10.22028/D291-39174
DOI: 10.1111/cgf.142628
Availability: http://nbn-resolving.org/urn:nbn:de:bsz:291--ds-391743; https://doi.org/10.22028/D291-39174; https://doi.org/10.1111/cgf.142628
Rights: openAccess ; CC BY 4.0 DEED Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.F20D6DA2
Database: BASE