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

Author: 
Date created: 
2021-06-25
Identifier: 
etd21468
Keywords: 
Computer graphics
Ray tracing
Real-time rendering
Global illumination
Resampled importance sampling
Reservoir sampling
Abstract: 

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.

Document type: 
Thesis
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
KangKang Yin
Eugene Fiume
Department: 
Applied Sciences: School of Computing Science
Thesis type: 
(Thesis) M.Sc.
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