The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
Open accessAbstract
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 Fr\'echet 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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Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
GAN（Generative Adversarial Nets）
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R. Q., Su, H., Kaichun, M., & Guibas, L. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.16 · Cited by 9,237
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365
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Zhou, L., Du, Y., & Wu, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00577 · Cited by 421
Learning Gradient Fields for Shape Generation
Cai, R., Yang, G., Averbuch-Elor, H., Hao, Z., Belongie, S., Snavely, N., & Hariharan, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58580-8_22 · Cited by 225
Computer Vision-Based Wood Identification: A Review
Silva, J., Bordalo, R., Pissarra, J., & de Palacios, P. (2022) · Forests · doi:10.3390/f13122041 · Cited by 37
Metadata
- Authors
- Shu, D., Park, S. W., & Kwon, J.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 3858-3867
- OpenAlex
- W2945795706
- Cited by
- 265
- References
- 47
- FWCI
- 33.87
- Type
- conference paper
- Language
- en
- Open access
- green
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