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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Bibliographic Details
Published in:	Computer graphics forum Vol. 37; no. 4; pp. 35 - 44
Main Authors:	Kuznetsov, Alexandr, Kalantari, Nima Khademi, Ramamoorthi, Ravi
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01.07.2018
Subjects:	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
Online Access:	Get full text
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