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Monte Carlo Denoising via Multi-scale Auxiliary Feature Fusion Guided Transformer
SessionRendering
DescriptionWe present an adversarial transformer-based Monte Carlo denoising network guided by multi-scale auxiliary feature. Extensive experiments demonstrate the method's superiority in quantitative metrics and visual perception compared with state-of-the-art methods.
Contributors
Bingyi Chen
Center for Future Media, University of Electronic Science and Technology of China
School of Computer Science and Engineering, University of Electronic Science and Technology of China
Zengyu Liu
Center for Future Media, University of Electronic Science and Technology of China
School of Computer Science and Engineering, University of Electronic Science and Technology of China
Li Yuan
Institute of Military Political Work, Academy of Military Sciences
Zhitao Liu
Center for Future Media, University of Electronic Science and Technology of China
School of Computer Science and Engineering, University of Electronic Science and Technology of China
Yi Li
Center for Future Media, University of Electronic Science and Technology of China
School of Computer Science and Engineering, University of Electronic Science and Technology of China
Guan Wang
Yangtze Delta Region Institute of University of Electronic Science and Technology of China
Ning Xie
Center for Future Media, University of Electronic Science and Technology of China
School of Computer Science and Engineering, UESCT
Event Type
Technical Communications
Technical Papers
TimeTuesday, 12 December 20235:30pm - 5:38pm
LocationMeeting Room C4.8, Level 4 (Convention Centre)
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