  In this paper, we propose a novel generative adversarial network (GAN) for 3D
point clouds generation, which is called tree-GAN. To achieve state-of-the-art
performance for multi-class 3D point cloud generation, a tree-structured graph
convolution network (TreeGCN) is introduced as a generator for tree-GAN.
Because TreeGCN performs graph convolutions within a tree, it can use ancestor
information to boost the representation power for features. To evaluate GANs
for 3D point clouds accurately, we develop a novel evaluation metric called
Frechet point cloud distance (FPD). Experimental results demonstrate that the
proposed tree-GAN outperforms state-of-the-art GANs in terms of both
conventional metrics and FPD, and can generate point clouds for different
semantic parts without prior knowledge.
