AOGAN: A generative adversarial network for screen space ambient occlusion
Keyword(s):
AbstractAmbient 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.
2021 ◽
Vol 4
(1)
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pp. 1-15
Keyword(s):
Real-time image carrier generation based on generative adversarial network and fast object detection
2020 ◽
Vol 17
(3)
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pp. 655-665
Keyword(s):
2020 ◽
Vol 150
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pp. 113244
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Keyword(s):
2020 ◽
Vol 34
(05)
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pp. 7708-7715