  Constructing high-quality generative models for 3D shapes is a fundamental
task in computer vision with diverse applications in geometry processing,
engineering, and design. Despite the recent progress in deep generative
modelling, synthesis of finely detailed 3D surfaces, such as high-resolution
point clouds, from scratch has not been achieved with existing approaches. In
this work, we propose to employ the latent-space Laplacian pyramid
representation within a hierarchical generative model for 3D point clouds. We
combine the recently proposed latent-space GAN and Laplacian GAN architectures
to form a multi-scale model capable of generating 3D point clouds at increasing
levels of detail. Our evaluation demonstrates that our model outperforms the
existing generative models for 3D point clouds.
