Quantifying the generative capabilities of variational autoencoders for 3D car point clouds

During each cycle of automotive development, large amounts of geometric data are generated as results of design studies and simulation tasks. Discovering hidden knowledge from this data and making it available to the development team strengthens the design process by utilizing historic information when creating novel products. To this end, we propose to use powerful geometric deep learning models that learn lowdimensional representation of the design data in an unsupervised fashion. Trained models allow to efficiently explore the design space, as well as to generate novel designs. One popular class of generative models are variational autoencoders, which have however been rarely applied to geometric data. Hence, we use a variational autoencoder for 3D point clouds (PC-VAE) and explore the model’s generative capabilities with a focus on the generation of realistic yet novel 3D shapes. We apply the PC-VAE to point clouds sampled from car shapes from a benchmark data set and employ quantitative measures to show that our PC-VAE generates realistic car shapes, wile returning a richer variety of unseen shapes compared to a baseline autoencoder. Finally, we demonstrate how the PC-VAE can be guided towards generating shapes with desired target properties by optimizing the parameters that maximize the output of a trained classifier for said target properties. We conclude that generative models are a powerful tool that may aid designers in automotive product development.