Papers by Thijs Vogels


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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

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SIGGRAPH 2017


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

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