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

Autor:	Shu, Dong Wook, Park, Sung Woo, Kwon, Junseok
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1905.06292 Zobrazit plný text záznamu View this record from Arxiv