Correlation-Aware Multiple Importance Sampling for Bidirectional Rendering Algorithms

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.

Automated summary

Combining diverse sampling techniques through multiple importance sampling is crucial for robustness in modern Monte Carlo light transport simulation. The proposal of a correlation-aware heuristic, based on known path densities required for MIS, can achieve significantly lower error compared to the balance heuristic, without incurring additional computational and memory overhead.