Three-dimensional (3D) point clouds are becoming an important part of the geospatial domain. During research on 3D point clouds, deep-learning models have been widely used for the classification and segmentation of 3D point clouds observed by airborne LiDAR. However, most previous studies used discriminative models, whereas few studies used generative models. Specifically, one unsolved problem is the synthesis of large-scale 3D point clouds, such as those observed in outdoor scenes, because of the 3D point clouds' complex geometric structure. In this paper, we propose a generative model for generating large-scale 3D point clouds observed from airborne LiDAR. Generally, because the training process of the famous generative model called generative adversarial network (GAN) is unstable, we combine a variational autoen-coder and GAN to generate a suitable 3D point cloud. We experimentally demonstrate that our framework can generate high-density 3D point clouds by using data from the 2018 IEEE GRSS Data Fusion Contest.