[1] [1] YANG B S, LIANG F X, HUANG R G. Progress, challenges and perspectives of 3D LiDAR point cloud processing［J］. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1509-1516.

[2] [2] YANG B S, LIU Y, DONG Z, et al. 3D local feature BKD to extract road information from mobile laser scanning point clouds［J］. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130: 329-343.

[3] [3] WU H B, YAO L B, XU Z R, et al. Road pothole extraction and safety evaluation by integration of point cloud and images derived from mobile mapping sensors［J］. Advanced Engineering Informatics, 2019, 42: 100936.

[8] [8] PAPON J, ABRAMOV A, SCHOELER M, et al. Voxel cloud connectivity segmentation-supervoxels for point clouds［C］//2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA: IEEE, 2013: 2027-2034.

[10] [10] SHI W J, RAJKUMAR R. Point-GNN: Graph neural network for 3D object detection in a point cloud［C］//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA. IEEE, 2020: 1708-1716.

[14] [14] NGUYEN H L, BELTON D, HELMHOLZ P. Planar surface detection for sparse and heterogeneous mobile laser scanning point clouds［J］. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151: 141-161.

[15] [15] LU X H, LIU Y H, LI K. Fast 3D line segment detection from unorganized point cloud［J］. ArXiv e-Prints, 2019: arXiv: 1901.02532.

[17] [17] LIN Y B, WANG C, ZHAI D W, et al. Toward better boundary preserved supervoxel segmentation for 3D point clouds［J］. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 143: 39-47.