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.

本文提出了一种名为 tree-GAN 的新型生成对抗网络用于 3D 点云生成，并引入树形图卷积网络 (TreeGCN) 作为 tree-GAN 的生成器以提高特征的表示能力，通过引入评估指标 FPD 来准确评估 3D 点云 GAN，实验结果表明了 tree-GAN 在传统度量和 FPD 方面都优于现有的 GAN，并且可以生成不同语义部分的点云。