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

Dong Wook Shu; Sung Woo Park; Junseok Kwon

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arXiv · 1905.06292

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

Dong Wook Shu·Sung Woo Park·Junseok Kwon

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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.

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BibTeX RIS

Dong Wook Shu, Sung Woo Park, Junseok Kwon. 2019-05-15. 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions. https://arxiv.org/abs/1905.06292

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