MS-GAN: 3D deep generative model for multi-species propeller parameterization and generation

In this study, we introduce a deep generative model, named Multi-Species Generative Adversarial Network (MS-GAN), which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces. In the development of MS-GAN, we extend the free-form deformation by incorporating principal component analysis to increase the non-linear deformation ability while maintaining geometric smoothness. The implicit information of multiple baselines is embedded in the feature extraction layers, to enhance the diversity and parameterization of multi-species dataset. Furthermore, Wasserstein GAN with a gradient penalty is used to ensure the stability and convergence of the training networks. Two experiments, ruled surfaces and propeller blade surfaces, are performed to demonstrate the advantages and superiorities of MS-GAN.