[1] Zhang J X, Lin X G, Liang X L. Advances and prospects of information extraction from point clouds[J]. Acta Geodaetica et Cartographica Sinica, 46, 1460-1469(2017).

[2] Zhang J X, Lin X G, Ning X G. SVM-based classification of segmented airborne LiDAR point clouds in urban areas[J]. Remote Sensing, 5, 3749-3775(2013).

[3] He M Y, Cheng Y L, Liao X J et al. Building extraction algorithm by fusing spectral and geometrical features[J]. Laser & Optoelectronics Progress, 55, 042803(2018).

[4] Niemeyer J, Rottensteiner F, Soergel U. Contextual classification of lidar data and building object detection in urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 152-165(2014).

[5] Su H, Maji S, Kalogerakis E et al. Multi-view convolutional neural networks for 3D shape recognition[C], 945-953(2015).

[6] Gu S T, Wang L, Ma Y X et al. Local feature description of LiDAR point cloud data based on hierarchical Mercator projection[J]. Acta Optica Sinica, 40, 2015001(2020).

[7] Le T, Duan Y. PointGrid: a deep network for 3D shape understanding[C], 9204-9214(2018).

[8] Maturana D, Scherer S. VoxNet: a 3D convolutional neural network for real-time object recognition[C], 922-928(2015).

[9] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C], 77-85(2017).

[10] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C], 5099-5108(2017).

[11] Bai J, Xu H J. MSP-Net: multi-scale point cloud classification network[J]. Journal of Computer-Aided Design & Computer Graphics, 31, 1917-1924(2019).

[12] Wang H T, Lei X D, Zhao Z Z. 3D deep learning classification method for airborne LiDAR point clouds fusing spectral information[J]. Laser & Optoelectronics Progress, 57, 122802(2020).

[13] Kowalczuk Z, Szymański K. Classification of objects in the LiDAR point clouds using deep neural networks based on the PointNet model[J]. IFAC-PapersOnLine, 52, 416-421(2019).

[14] Zhao Z Y, Cheng Y L, Shi X S et al. Terrain classification of LiDAR point cloud based on multi-scale features and PointNet[J]. Laser & Optoelectronics Progress, 56, 052804(2019).

[15] Shi X S, Cheng Y L, Xue D D et al. Object classification method for multi-source fusion point clouds based on Point-Net[J]. Laser & Optoelectronics Progress, 57, 081019(2020).

[16] Lei X D, Wang H T, Zhao Z Z. Small-sample airborne LiDAR point cloud classification based on transfer learning and fully convolutional network[J]. Chinese Journal of Lasers, 48, 1610001(2021).

[17] Wang Y, Sun Y B, Liu Z W et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 38, 1-12(2019).

[18] Zhang Y X, Rabbat M. A graph-CNN for 3D point cloud classification[C], 6279-6283(2018).

[19] Wang J A, He J, Pang D W. Point cloud classification and segmentation network based on dynamic graph convolutional network[J]. Laser & Optoelectronics Progress, 58, 1215008(2021).

[20] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015).

[21] Wen C C, Yang L N, Li X et al. Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 50-62(2020).