Atmospheric cloud representation methods in computer graphics: A review

Cloud representation is one of the important components in the atmospheric cloud visualization system. Lack of review papers on the cloud representation methods available in the area of computer graphics has directed towards the difficulty for researchers to understand the appropriate solutions. Therefore, this paper aims to provide a comprehensive review of the atmospheric cloud representation methods that have been proposed in the computer graphics domain, involving the classical and the current state-of-the-art approaches. The reviewing process was conducted by searching, selecting, and analyzing the prominent articles collected from online digital libraries and search engines. We highlighted the taxonomic classification of the existing cloud representation methods in solving the atmospheric cloud-related problems. Finally, research issues and directions in the area of cloud representations and visualization have been discussed. This review would be significantly beneficial for researchers to clearly understand the general picture of the existing methods and thus helping them in choosing the best-suited approach for their future research and development

Modeling Detailed Cloud Scene from Multi-source Images

Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For example, natural images are usually used to model small-scale cloud with details, and satellite images and WRF data are used to model large scale cloud without details. To construct large-scale cloud scene with details, we propose a novel method to extract relevant cloud information from both satellite and natural images. Experiments show our method can produce more detailed cloud scene comparing with existing methods

Modeling detailed cloud scene from multi-source images.

Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For example, natural images are usually used to model small-scale cloud with details, and satellite images and WRF data are used to model large scale cloud without details. To construct large-scale cloud scene with details, we propose a novel method to extract relevant cloud information from both satellite and natural images. Experiments show our method can produce more detailed cloud scene comparing with existing methods