Adaptive Incident Radiance Field Sampling and Reconstruction Using Deep Reinforcement Learning

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Serious noise affects the rendering of global illumination using Monte Carlo (MC) path tracing when insufficient samples are used. The two common solutions to this problem are filtering noisy inputs to generate smooth but biased results and sampling the MC integrand with a carefully crafted probability distribution function (PDF) to produce unbiased results. Both solutions benefit from an efficient incident radiance field sampling and reconstruction algorithm. This study proposes a method for training quality and reconstruction networks (Q- and R-networks, respectively) with a massive offline dataset for the adaptive sampling and reconstruction of first-bounce incident radiance fields. The convolutional neural network (CNN)-based R-network reconstructs the incident radiance field in a 4D space, whereas the deep reinforcement learning (DRL)-based Q-network predicts and guides the adaptive sampling process. The approach is verified by comparing it with state-of-the-art unbiased path guiding methods and filtering methods. Results demonstrate improvements for unbiased path guiding and competitive performance in biased applications, including filtering and irradiance caching.
Publisher
ASSOC COMPUTING MACHINERY
Issue Date
2020-02
Language
English
Article Type
Article
Citation

ACM TRANSACTIONS ON GRAPHICS, v.39, no.1

ISSN
0730-0301
DOI
10.1145/3368313
URI
http://hdl.handle.net/10203/274705
Appears in Collection
CS-Journal Papers(저널논문)
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