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Research Article | Open Access

AOGAN: A generative adversarial network for screen space ambient occlusion

Lei Ren1, Ying Song1,2,3( )
Zhejiang Sci-Tech University, Hangzhou 310018, China
2011 Collaborative Innovation Center for Garment Personal Customization of Zhejiang Province, China
Key Lab of Silk and Culture Heritage and Products Design Digital Technology, Ministry of Culture and Tourism, China
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Abstract

Ambient occlusion (AO) is a widely-used real-time rendering technique which estimates light intensity on visible scene surfaces. Recently, a number of learning-based AO approaches have been proposed, which bring a new angle to solving screen space shading via a unified learning framework with competitive quality and speed. However, most such methods have high error for complex scenes or tend to ignore details. We propose an end-to-end generative adversarial network for the production of realistic AO, and explore the importance of perceptual loss in the generative model to AO accuracy. An attention mechanism is also described to improve the accuracy of details, whose effectiveness is demonstrated on a wide variety of scenes.

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Keywords

ambient occlusion (AO)attention me-chanismgenerative adversarial network (GAN)perceptual loss

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Computational Visual Media
Volume 8 Issue 3,
September 2022
Pages 483-494
DOI: 10.1007/s41095-021-0248-2

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Cite this article:
Ren L, Song Y. AOGAN: A generative adversarial network for screen space ambient occlusion. Computational Visual Media, 2022, 8(3): 483-494. https://doi.org/10.1007/s41095-021-0248-2

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Received: 13 May 2021
Accepted: 13 July 2021
Published: 06 January 2022
© The Author(s) 2021.

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