World-Space Spatiotemporal Path Resampling for Path Tracing

Abstract

With the advent of hardware-accelerated ray tracing, more and more real-time rendering applications tend to render images with ray-traced global illumination (GI). However, the low sample counts at real-time framerates bring enormous challenges to existing path sampling methods. Recent work (ReSTIR GI) samples indirect illumination effectively with a dramatic bias reduction. However, as a screen-space based path resampling approach, it can only reuse the path at the first bounce and brings subtle benefits for complex scenes. To this end, we propose a world-space based spatiotemporal path resampling approach. Our approach caches more path samples into a world-space grid, which allows reusing sub-path starting from non-primary path vertices. Furthermore, we introduce a practical normal-aware hash grid construction approach, providing more efficient candidate samples for path resampling. Eventually, our method achieves improvements ranging from 16.6% to 41.9% in terms of mean squared errors (MSE) compared against the previous method with only 4.4% ~ 8.4% extra time cost.