[3] [3] Lalonde J F, Unnikrishnan R, Vandapel N, et al. Scale selection for classification of point-sampled 3D surfaces[C]//Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM′05). IEEE, 2005: 285-292.

[4] [4] Gao Z, Liu X. Support vector machine and object-oriented classification for urban impervious surface extraction from satellite imagery[C]//2014 The Third International Conference on Agro-Geoinformatics. IEEE, 2014: 1-5.

[5] [5] Wang C, Shu Q, Wang X, et al. A random forest classifier based on pixel comparison features for urban LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 148: 75-86.

[6] [6] Chua L O, Roska T. The CNN paradigm[J]. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1993, 40(3): 147-156.

[7] [7] Scarselli F, Gori M, Tsoi A C, et al. The graph neural network model[J]. IEEE Transactions on Neural Networks, 2008, 20(1): 61-80.

[8] [8] Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization[J/OL]. arXiv Preprint arXiv: 1409.2329, 2014.

[9] [9] Su H, Maji S, Kalogerakis E, et al. Multi-view convolutional neural networks for 3d shape recognition[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 945-953.

[10] [10] Maturana D, Scherer S. Voxnet: A 3d convolutional neural network for real-time object recognition[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2015: 922-928.

[11] [11] Wu Z, Song S, Khosla A, et al.3d shapenets: A deep representation for volumetric shapes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1912-1920.

[12] [12] Zhang C, Wan H, Shen X, et al. PVT: Point-voxel transformer for point cloud learning[J]. International Journal of Intelligent Systems, 2022, 37(12): 11985-12008.

[13] [13] Qi C R, Su H, Mo K, et al. Pointnet: Deep learning on point sets for 3D classification and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 652-660.

[14] [14] Qi C R, Yi L, Su H, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[J/OL]. https:/arxiv.org/abs/1706.02413.

[15] [15] Mohammadi S S, Wang Y, Del Bue A. Pointview-gcn: 3d shape classification with multi-view point clouds[C]//2021 IEEE International Conference on Image Processing (ICIP), IEEE, 2021: 3103-3107.

[16] [16] Simonovsky M, Komodakis N. Dynamic edge-conditioned filters in convolutional neural networks on graphs[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 3693-3702.

[17] [17] Wang Y, Sun Y, Liu Z, et al. Dynamic graph cnn for learning on point clouds[J]. Acm Transactions On Graphics (tog), 2019, 38(5): 1-12.

[18] [18] Li G, Mller, Matthias, et al. DeepGCNs: making GCNs Go as deep as CNNs[J/OL]. https://arxiv.org/abs/1910.06849.

[19] [19] Shi W, Rajkumar R. Point-gnn: graph neural network for 3D object detection in a point cloud[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 1711-1719.

[20] [20] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017, 1706: 03762.

[21] [21] Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.

[22] [22] Wu Z, Song S, Khosla A, et al.3D shapenets: a deep representation for volumetric shapes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1912-1920.