Cumulus clouds are important elements in creating virtual outdoor scenes. Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud. Image-based modeling is an efficient method to solve this problem. Because of the complexity of cloud shapes, the task of modeling the cloud from a single image remains in the development phase.

In this study, a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image. The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network. First, a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder. Then, 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 images. To train the presented models, we constructed a 3D cumulus dataset that included 200 3D cumulus models. These cumulus clouds were rendered under different lighting parameters.

The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches. Furthermore, some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.

The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes. The presented reconstruction architecture models a cloud from a single image. Experiments demonstrated the effectiveness of the two models.