[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).
[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).
[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).
[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).
[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).
[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).