Deep Compositional Denoising for High-quality Monte Carlo Rendering
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
We propose a deep-learning method for automatically decomposing noisy Monte Carlo renderings into components that kernelpredicting denoisers can denoise more effectively. In our model, a neural decomposition module learns to predict noisy components and corresponding feature maps, which are consecutively reconstructed by a denoising module. The components are predicted based on statistics aggregated at the pixel level by the renderer. Denoising these components individually allows the use of per-component kernels that adapt to each component's noisy signal characteristics. Experimentally, we show that the proposed decomposition module consistently improves the denoising quality of current state-of-the-art kernel-predicting denoisers on large-scale academic and production datasets.
Description
@article{10.1111:cgf.14337,
journal = {Computer Graphics Forum},
title = {{Deep Compositional Denoising for High-quality Monte Carlo Rendering}},
author = {Zhang, Xianyao and Manzi, Marco and Vogels, Thijs and Dahlberg, Henrik and Gross, Markus and Papas, Marios},
year = {2021},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14337}
}