[1] Hu C J, Liu J Z, Fang L. Unsupervised domain adaptation for outdoor point cloud semantic segmentation[J]. Computer and Modernization, 74-79, 86, 91(2024).

[2] Tsai Y H, Hung W C, Schulter S et al. Learning to adapt structured output space for semantic segmentation[C], 7472-7481(2018).

[3] Qin C, You H X, Wang L C et al. PointDAN: a multi-scale 3D domain adaption network for point cloud representation[C](2019).

[4] Wang H, Jiang Z, Yi L et al. Rethinking sampling in 3D point cloud generative adversarial networks[EB/OL]. https://arxiv.org/abs/2006.07029

[5] Ganin Y, Ustinova E, Ajakan H et al. Domain-adversarial training of neural networks[M]. Domain adaptation in computer vision applications, 189-209(2017).

[6] Sauder J, Sievers B. Self-supervised deep learning on point clouds by reconstructing space[EB/OL]. https://arxiv.org/abs/1901.08396v2

[7] Chen J T, Zhang Y, Huang K et al. Self-supervised boundary point prediction task for point cloud domain adaptation[J]. IEEE Robotics and Automation Letters, 8, 5878-5885(2023).

[8] Zheng C J, Zheng Z. 3D point cloud classification based on maximum classifier discrepancy domain adaptation method[J]. Information and Control, 52, 588-597(2023).

[9] Liang H X, Fan H H, Fan Z W et al. Point cloud domain adaptation via masked local 3D structure prediction[M]. Computer vision-ECCV 2022, 13663, 156-172(2022).

[10] Park J J, Florence P, Straub J et al. DeepSDF: learning continuous signed distance functions for shape representation[C], 165-174(2019).

[11] Schubert E, Sander J, Ester M et al. DBSCAN revisited, revisited: why and how you should (still) use DBSCAN[J]. ACM Transactions on Database Systems, 42, 19(2017).

[12] Zhang C Y, Han L. Obstacle detection of LiDAR based on optimized DBSCAN[J]. Laser & Optoelectronics Progress, 59, 1228008(2022).

[13] Zhang H Y, Cisse M, Danuphin Y N et al. mixup: beyond empirical risk minimization[C], 528-536(2018).

[14] Achituve I, Maron H, Chechik G. Self-supervised learning for domain adaptation on point clouds[C], 123-133(2021).

[15] Zhang L J, Bi Y W, Liu Y W et al. Augmented edge graph convolutional networks for semantic segmentation of 3D point clouds[J]. Laser & Optoelectronics Progress, 61, 0837009(2024).

[16] Boscaini D, Poiesi F. PatchMixer: Rethinking network design to boost generalization for 3D point cloud understanding[J]. Image and Vision Computing, 137, 104768(2023).

[17] Liu S L, Luo X Y, Fu K X et al. A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation[J]. Frontiers of Computer Science, 17, 176708(2023).

[18] Park J, Seo H, Yang E. PC-adapter: topology-aware adapter for efficient domain adaption on point clouds with rectified pseudo-label[C], 11496-11506(2023).

[19] Guo L L, Wang S L, Dai H S et al. Initial orbit determination for space-based optical surveillance of space debris[J]. Acta Optica Sinica, 44, 2412002(2024).

[20] Miao L Y, Li F. Retinal vessel segmentation based on dynamic feature graph convolutional network[J]. Chinese Journal of Lasers, 51, 1507202(2024).