Optimal Multiple Importance Resampling
Optimal Spatial Reuse for Monte Carlo Light Transport Simulation
W. Narchi (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Elmar Eisemann (TU Delft - Computer Graphics and Visualisation)
M. Molenaar (TU Delft - Computer Graphics and Visualisation)
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Abstract
Ray tracing has experienced increasing adoption in various spaces of computer graphics. The ReSTIR (Reservoir-based Spatiotemporal Importance Resampling) family of techniques has enabled several orders of magnitude speedups in light transport simulation algorithms which rely on ray tracing.
We introduce an extension to WRS (Weighted Reservoir Sampling), a key component of ReSTIR, to reduce the occurrence of duplicate samples in multi-sample reservoirs. Further, we show how samples from multiple reservoirs can be combined in an MIS-style estimator as opposed to resampling from them. Lastly, we combine these two components to compute optimal weights for this estimator in a similar vein to OMIS (Optimal Multiple Importance Sampling).
Our direct lighting proof of concept implementation demonstrates the efficacy of our WRS extension, lowering the variance of ReSTIR, particularly with difficult lighting arrangements. Further, our MIS-style estimator shows a significant improvement compared to ReSTIR. In problem domains where resampling produces a function value for integration, such as path tracing, this comes at no additional cost. Lastly however, optimal weights do not appear to be beneficial for our approach.