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Cumulus cloud modeling from images based on VAE-GAN

Virtual Reality & Intelligent Hardware 2021年第2期3卷 171-181页

作者： Zili ZHANG Yunchi CEN Fan ZHANG Xiaohui LIANG State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Shijiazhuang UniversityShijiazhuang 050035China

Background Cumulus clouds are important elements in creating virtual outdoor *** cumulus clouds that have a specific shape is difficult owing to the fluid nature of the ***-based modeling is an efficient method to solve this *** of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development *** In this study,a deep learning-based method was developed to address the problem of modeling 3d cumulus clouds from a single *** method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial ***,a 3d cloud shape is mapped into a unique hidden space using the proposed ***,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered *** train the presented models,we constructed a 3d cumulus dataset that included 2003d cumulus *** cumulus clouds were rendered under different lighting *** The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3d cumulus shapes than existing ***,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction *** The proposed autoencoder network learns the latent space of 3d cumulus cloud *** presented reconstruction architecture models a cloud from a single *** demonstrated the effectiveness of the two models.

关键词： 3d cloud model 3d autoencoder network Generative adversarial network

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