A point cloud self-learning network based on contrastive learning for classification and segmentation
Author:
Funder
Natural Science Foundation of Shanghai
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-03248-4.pdf
Reference64 articles.
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3. Zheng, Y., Li, Y., Yang, S., Lu, H.: Global-pbnet: a novel point cloud registration for autonomous driving. IEEE Trans. Intell. Transp. Syst. 23(11), 22312–22319 (2022)
4. Geng, Z., Sabbaghi, A., Bidanda, B.: Automated variance modeling for three-dimensional point cloud data via Bayesian neural networks. IISE Trans. 55(9), 912–925 (2023)
5. Nguyen, V.-T., Fournier, R.A., Côté, J.-F., Pimont, F.: Estimation of vertical plant area density from single return terrestrial laser scanning point clouds acquired in forest environments. Remote Sens. Environ. 279, 113115 (2022)
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