Edge-guided generative network with attention for point cloud completion
Author:
Funder
Natural Science Foundation of Xinjiang Uygur Autonomous Region
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03364-9.pdf
Reference43 articles.
1. Chen, S., Liu, B., Feng, C., Vallespi-Gonzalez, C., Wellington, C.: 3d point cloud processing and learning for autonomous driving: impacting map creation, localization, and perception. IEEE Signal Process. Mag. 38(1), 68–86 (2020)
2. Agarwal, S., Bhowmick, B.: 3d point cloud registration with shape constraint. In: 2017 IEEE International Conference on Image Processing (ICIP), 2199–2203 (2017). IEEE
3. Li, C., Guan, Y., Yang, S., Li, Y.: A dynamic learning framework integrating attention mechanism for point cloud registration. Vis. Comput. (2023). https://doi.org/10.1007/s00371-023-03118-z
4. Wang, S., Kang, Z., Chen, L., Guo, Y., Zhao, Y., Chai, Y.: Partial point cloud registration algorithm based on deep learning and non-corresponding point estimation. Vis. Comput. (2023). https://doi.org/10.1007/s00371-023-03103-6
5. Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., Heng, P.-A.: Pu-net: point cloud upsampling network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2790–2799 (2018)
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