Correlation-aware multiple importance sampling for bidirectional rendering algorithms


A scene featuring complex indirect illumination (lamp shade) and caustics (glass) — a prime use-case of bidirectional algorithms. We show two such methods: bidirectional path tracing with splitting (top row), and vertex connection and merging (bottom row). (a) Both exhibit problems with MIS in this scenario, due to correlation by shared path prefixes. (b) Our simple heuristic solves these problems.


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.

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BibTeX reference

  author = {Pascal Grittmann and Iliyan Georgiev and Philipp Slusallek},
  title = {Correlation-Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms},
  journal = {Comput. Graph. Forum (EUROGRAPHICS 2021)},
  volume = {40},
  number = {2},
  year = {2021}