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AOGAN:A generative adversarial network for screen space ambient occlusion
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作者 Lei Ren Ying Song 《Computational Visual Media》 SCIE EI CSCD 2022年第3期483-494,共12页
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 angl... 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. 展开更多
关键词 ambient occlusion(AO) attention mechanism generative adversarial network(GAN) perceptual loss
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