A survey on deep learning-based Monte Carlo denoising

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Monte Carlo (MC) integration is used ubiquitously in realistic image synthesis because of its flexibility and generality. However, the integration has to balance estimator bias and variance, which causes visually distracting noise with low sample counts. Existing solutions fall into two categories, in-process sampling schemes and post-processing reconstruction schemes. This report summarizes recent trends in the post-processing reconstruction scheme. Recent years have seen increasing attention and significant progress in denoising MC rendering with deep learning, by training neural networks to reconstruct denoised rendering results from sparse MC samples. Many of these techniques show promising results in real-world applications, and this report aims to provide an assessment of these approaches for practitioners and researchers.
Publisher
SPRINGERNATURE
Issue Date
2021-06
Language
English
Article Type
Review
Citation

COMPUTATIONAL VISUAL MEDIA, v.7, no.2, pp.169 - 185

ISSN
2096-0433
DOI
10.1007/s41095-021-0209-9
URI
http://hdl.handle.net/10203/285449
Appears in Collection
CS-Journal Papers(저널논문)
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