ML Anthology

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

Dong Wook Shu, Sung Woo Park, Junseok Kwon
ICCV 2019
doi:10.1109/ICCV.2019.00396 /iccv/2019/shu2019iccv-3d/

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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Cite

Text

Shu et al. "3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00396

Markdown

[Shu et al. "3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/shu2019iccv-3d/) doi:10.1109/ICCV.2019.00396

BibTeX

@inproceedings{shu2019iccv-3d,
  title     = {{3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions}},
  author    = {Shu, Dong Wook and Park, Sung Woo and Kwon, Junseok},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year      = {2019},
  doi       = {10.1109/ICCV.2019.00396},
  url       = {https://mlanthology.org/iccv/2019/shu2019iccv-3d/}
}