Next event estimation via reservoir-based spatio-temporal importance resampling

Thesis type
(Thesis) M.Sc.
Date created
Author: Badke, Adam
The arrival of dedicated GPU ray-tracing acceleration hardware has renewed demand for Monte Carlo path tracing algorithms optimized for interactive applications. Yet even with hardware support, rendering high quality ray-traced images in real-time remains challenging. We develop a biased Monte Carlo forward path tracing algorithm capable of rendering realistic, globally-illuminated scenes with thousands of dynamic area lights, at interactive frame rates on consumer-grade GPUs, that produces images with significantly lower error and visual noise than other, state-of-the-art techniques. It builds upon Talbot's "Importance Resampling for Global Illumination," and extends Bitterli et al.'s "Reservoir-based Spatio-Temporal Importance Resampling" beyond the screen-space computation of direct lighting, to computing forward path traced global illumination via light paths of variable lengths. To reduce variance and improve local lighting estimates, light samples are reused between geometrically coherent, temporally and spatially neighboring light path vertices. Screen-space techniques, and a spherically-projected buffer for enabling sample reuse are explored.
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Supervisor or Senior Supervisor
Thesis advisor: Yin, KangKang
Thesis advisor: Fiume, Eugene
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