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Correlation‐Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms
Author(s) -
Grittmann Pascal,
Georgiev Iliyan,
Slusallek Philipp
Publication year - 2021
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.142628
Subject(s) - computer science , heuristics , robustness (evolution) , rendering (computer graphics) , path tracing , algorithm , sampling (signal processing) , importance sampling , path (computing) , heuristic , distributed ray tracing , correlation , monte carlo method , mathematical optimization , artificial intelligence , mathematics , computer vision , biochemistry , chemistry , statistics , geometry , filter (signal processing) , gene , programming language , operating system
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.