Deep Shading: Convolutional Neural Networks for Screen Space Shading
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
In computer vision, convolutional neural networks (CNNs) achieve unprecedented performance for inverse problems where RGB pixel appearance is mapped to attributes such as positions, normals or reflectance. In computer graphics, screen space shading has boosted the quality of real-time rendering, converting the same kind of attributes of a virtual scene back to appearance, enabling effects like ambient occlusion, indirect light, scattering and many more. In this paper we consider the diagonal problem: synthesizing appearance from given per-pixel attributes using a CNN. The resulting Deep Shading renders screen space effects at competitive quality and speed while not being programmed by human experts but learned from example images.
Description
@article{10.1111:cgf.13225,
journal = {Computer Graphics Forum},
title = {{Deep Shading: Convolutional Neural Networks for Screen Space Shading}},
author = {Nalbach, Oliver and Arabadzhiyska, Elena and Mehta, Dushyant and Seidel, Hans-Peter and Ritschel, Tobias},
year = {2017},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13225}
}