[1] Liu J N, Dong Y, Zhan J et al. Thoughts and suggestions on autonomous driving map policy[J]. Strategic Study of CAE, 21, 92-97(2019).

[2] Li X L, Zhou Y E, Bi T F et al. Review on key technologies of lightweight type-aware LiDAR[J]. Chinese Journal of Lasers, 49, 1910002(2022).

[3] Yan L, Ren D W, Xie H et al. Fusion method of LiDAR point cloud and dense matching point cloud[J]. Chinese Journal of Lasers, 49, 0910003(2022).

[4] Mao Y R, Chen Y R, Liu Y D. Study on road marking extraction method based on mobile LIDAR point cloud[J]. Journal of Highway and Transportation Research and Development, 36, 127-135(2019).

[5] Zheng S F, Wang S D, Zhang C Y et al. Extraction and classification of urban road marking lines based on point cloud features[J]. Laser Technology, 48, 27-33(2024).

[6] Yao L B, Qin C C, Zhang S H et al. Road marking extraction and semantic correlation based on vehicle-borne laser point cloud[J]. Acta Geodaetica et Cartographica Sinica, 49, 480-488(2020).

[7] Yan L, Li Z. Road marking extraction from mobile LiDAR data[J]. Remote Sensing Information, 33, 1-6(2018).

[8] Yao L, Qin C, Chen Q et al. Automatic extraction and recognition of road markings based on vehicle laser point cloud[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 313-319(2020).

[9] Li W G, Fan X, Mei Y et al. Road marking extraction method based on vehicle laser point cloud[J]. Chinese Journal of Lasers, 49, 0710001(2022).

[10] Zheng K, Zhou C, Yang L. Urban road marking extraction based on vehicle-borne laser scanning point cloud data[J]. Geomatics & Spatial Information Technology, 47, 163-166(2024).

[11] Zhang J Y, Zhao X L, Chen Z. Review of semantic segmentation of point cloud based on deep learning[J]. Laser & Optoelectronics Progress, 57, 040002(2020).

[12] Huang G, Liu X L. Automatic extraction and classification of road markings based on deep learning[J]. Chinese Journal of Lasers, 46, 0804002(2019).

[13] Lagahit M L R, Matsuoka M. Focal combo loss for improved road marking extraction of sparse mobile LiDAR scanning point cloud-derived images using convolutional neural networks[J]. Remote Sensing, 15, 597-612(2023).

[14] Niu P T, Cao Y, Zhang E C et al. Automatic road marking extraction method based on vehicle LiDAR point cloud and panoramic image[J]. Bulletin of Surveying and Mapping, 67-71, 139(2023).

[15] Ye Y. Automatic extraction of urban road markings based on point cloud data[J]. Geomatics & Spatial Information Technology, 45, 226-229(2022).

[16] 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).

[17] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[EB/OL]. http://arxiv.org/abs/1706.02413v1

[18] Li Y Y, Bu R, Sun M et al. Pointcnn: convolution on x-transformed points[C], 820-830(2018).

[19] Xu J, Liu H, Shen Y et al. Point clouds classification and segmentation for nursery trees based on improved PointNet++ model[J]. Chinese Journal of Lasers, 51, 0810001(2024).

[20] Hu Q Y, Yang B, Xie L H et al. RandLA-net: efficient semantic segmentation of large-scale point clouds[C], 11105-11114(2020).

[21] Shuang F, Huang X W, Li Y et al. A survey of large-scale point cloud semantic segmentation based on deep learning[J]. Science of Surveying and Mapping, 48, 195-209(2023).

[22] Tan W K, Qin N N, Ma L F et al. Toronto-3D: a large-scale mobile LiDAR dataset for semantic segmentation of urban roadways[C], 797-806(2020).

[23] Yang B S, Han X, Dong Z. Point cloud benchmark dataset WHU-TLS and WHU-MLS for deep learning[J]. National Remote Sensing Bulletin, 25, 231-240(2021).