We introduce a deep learning approach for denoising Monte Carlo-rendered images that produces high-quality results suitable for production. We train a convolutional neural network to learn the complex relationship between noisy and reference data across a large set of frames with varying distributed e ects from the film Finding Dory (le ). The trained network can then be applied to denoise new images from other films with significantly di erent style and content, such as Cars 3 (right), with production-quality results. Regression-based algorithms have shown to be good at denoising Monte Carlo (MC) renderings by leveraging its inexpensive by-products (e.g., feature bu ers). However, when using higher-order models to handle complex cases, these techniques often over t to noise in the input. For this reason, supervised learning methods have been proposed that train on a large collection of reference examples, but they use explicit lters that limit their denoising ability. To address these problems, we propose a novel, supervised learning approach that allows the ltering kernel to be more complex and general by leveraging a deep convolutional neural network (CNN) architecture. In one embodiment of our framework, the CNN directly predicts the nal denoised pixel value as a highly non-linear combination of the input features. In a second approach, we introduce a novel, kernel-prediction network which uses the CNN to estimate the local weighting kernels used to compute each denoised pixel from its neighbors. We train and evaluate our * Joint rst authors Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro t or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). © 2017 Copyright held by the owner/author(s). 0730-0301/2017/7-ART97 $15.00 DOI: http://dx.doi.org/10.1145/3072959.3073708 networks on production data and observe improvements over state-of-theart MC denoisers, showing that our methods generalize well to a variety of scenes. We conclude by analyzing various components of our architecture and identify areas of further research in deep learning for MC denoising.
Recently, Bako et al. REF further used a convolutional neural network (CNN) to enable a more complex kernel.
Kernel-predicting convolutional networks for denoising Monte Carlo renderings
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Computer Science