Deep Compositional Denoising for High-quality Monte Carlo Rendering

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
} }
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