# 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions

## TL;DR

This paper introduces tree-GAN, a novel 3D point cloud generative model using tree-structured graph convolutions, achieving superior quality and diversity in generated point clouds through a new evaluation metric.

## Contribution

It presents a new GAN architecture with tree-structured graph convolutions for improved 3D point cloud generation and a novel metric for evaluation.

## Key findings

- Tree-GAN outperforms existing GANs in quality and diversity.
- The proposed FPD metric effectively evaluates 3D point cloud generation.
- Tree-GAN can generate semantic part-specific point clouds without prior knowledge.

## Abstract

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.