Importance resampling for global illumination

Abstract

This paper develops importance resampling into a variance reduction technique for Monte Carlo integration. Importance resampling is a sample generation technique that can be used to generate more equally weighted samples for importance sampling. This can lead to significant variance reduction over standard importance sampling for common rendering problems. We show how to select the importance resampling parameters for near optimal variance reduction. We demonstrate the robustness of this technique on common global illumination problems and achieve a 10%-70% variance reduction over standard importance sampling for direct lighting. We conclude that further variance reduction could be achieved with cheaper sampling methods.