Deep Adaptive Sampling for Low Sample Count Rendering

Recently, deep learning approaches have proven successful at removing noise from Monte Carlo (MC) rendered images at extremely low sampling rates, e.g., 1–4 samples per pixel (spp). While these methods provide dramatic speedups, they operate on uniformly sampled MC rendered images. However, the full...

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Vydané v:	Computer graphics forum Ročník 37; číslo 4; s. 35 - 44
Hlavní autori:	Kuznetsov, Alexandr, Kalantari, Nima Khademi, Ramamoorthi, Ravi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Blackwell Publishing Ltd 01.07.2018
Predmet:	Adaptive sampling Artificial neural networks Back propagation CCS Concepts Computer simulation Computing methodologies → Ray tracing Deep learning Ground truth Image processing Image reconstruction Machine learning Neural networks Noise reduction Pixels Sampling techniques Training
ISSN:	0167-7055, 1467-8659
On-line prístup:	Získať plný text
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