Point cloud upsampling via a coarse-to-fine network with transformer-encoder
Author:
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03535-8.pdf
Reference28 articles.
1. Li, R., Li, X., Heng, P.-A., Fu, C.-W.: Point cloud upsampling via disentangled refinement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 344–353 (2021)
2. Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3d point clouds: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43(12), 4338–4364 (2020)
3. Qian, G., Abualshour, A., Li, G., Thabet, A., Ghanem, B.: Pu-gcn: point cloud upsampling using graph convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11683–11692 (2021)
4. Yifan, W., Wu, S., Huang, H., Cohen-Or, D., Sorkine-Hornung, O.: Patch-based progressive 3d point set upsampling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5958–5967 (2019)
5. Li, R., Li, X., Fu, C.-W., Cohen-Or, D., Heng, P.-A.: Pu-gan: a point cloud upsampling adversarial network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7203–7212 (2019)
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