Publikation
Efficiency-aware multiple importance sampling for bidirectional rendering algorithms
Pascal Grittmann; Ömercan Yazici; Iliyan Georgiev; Philipp Slusallek
In: ACM Transactions on Graphics (Proceedings of SIGGRAPH 2022). ACM Siggraph (Siggraph-2022), August 8-11, Vancouver, Canada, Online, 7/2022.
Zusammenfassung
Multiple importance sampling (MIS) is an indispensable tool in light-transport
simulation. It enables robust Monte Carlo integration by combining samples
from several techniques. However, it is well understood that such a combination is not always more efficient than using a single sampling technique.
Thus a major criticism of complex combined estimators, such as bidirectional
path tracing, is that they can be significantly less efficient on common scenes
than simpler algorithms like forward path tracing. We propose a general
method to improve MIS efficiency: By cheaply estimating the efficiencies of
various technique and sample-count combinations, we can pick the best one.
The key ingredient is a numerically robust and efficient scheme that uses
the samples of one MIS combination to compute the efficiency of multiple
other combinations. For example, we can run forward path tracing and use
its samples to decide which subset of VCM to enable, and at what sampling
rates. The sample count for each technique can be controlled per-pixel or
globally. Applied to VCM, our approach enables robust rendering of complex
scenes with caustics, without compromising efficiency on simpler scenes.