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Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novak, Alex Harvill, Pradeep Sen, Tony DeRose, Fabrice Rousselle July 2017 Regression-based algorithms have shown to be good at denoising Monte Carlo (MC) renderings by leveraging its inexpensive by-products (e.g., feature buffers). However, when using higher-order models to handle complex cases, these techniques often overfit to noise in the input. For this reason, supervised learning methods have been proposed that ... more Paper (PDF) SIGGRAPH 2017 |
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Denoising with Kernel Prediction and Asymmetric Loss Functions Thijs Vogels, Fabrice Rouselle, Brian McWilliams, Gerhard Roethlin, Alex Harvill, David Adler, Mark Meyer, Jan Novak May 2018 We present a modular convolutional architecture for denoising rendered images. We expand on the capabilities of kernel-predicting networks by combining them with a number of task-specific modules, and optimizing the assembly using an asymmetric loss. The source aware encoder - the first module in the assembly - extracts ... more Paper (PDF) |