[4] [4] Wang Y, Zhang S S, Wan B L, et al. Point cloud and visual feature-based tracking method for an augmented reality-aided mechanical assembly system[J]. The International Journal of Advanced Manufacturing Technology, 2018, 99(9): 2341-2352.

[5] [5] Barrile V, Candela G, Fotia A. Point cloud segmentation using image processing techniques for structural analysis[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019, 42: 187-193.

[6] [6] Wang X H, Chen H W, Wu L S. Feature extraction of point clouds based on region clustering segmentation[J]. Multimedia Tools and Applications, 2020, 79: 11861-11889.

[7] [7] Zhao B F, Hua X H, Yu K G, et al. Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(11): 7890-7907.

[8] [8] WANG S L, WANG D K, Li C Y, et al. Clustering by fast search and find of density peaks with data field[J]. Chinese Journal of Electronics, 2016, 25(3): 397-402.

[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 (ICCV). Santiago: CVF, 2015: 945-953.

[10] [10] LUO C, LI X, CHENG N, et al. MVP-Net: multiple view pointwise semantic segmentation of Large-Scale point clouds[EB/OL]. arXiv preprint arXiv: 2201.12769.2022.

[11] [11] 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). Hamburg: IEEE, 2015: 922-928.

[12] [12] Riegler G, Ulusoy A O, Geiger A. OctNet: learning deep 3D representations at high resolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: CVF, 2017: 3577-3586.

[13] [13] Charles R Q, Su H, Kaichun M, 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 (CVPR). Honolulu: CVF, 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[EB/OL]. (2017-06-07). https://arxiv.org/abs/1706.02413.

[15] [15] Thomas H, Qi C R, Deschaud J E, et al. KPConv: flexible and deformable convolution for point clouds[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: CVF, 2019: 6411-6420.

[16] [16] HU Q Y, YANG B, XIE L H, et al. Randla-net: efficient semantic segmentation of large-scale point clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: CVF, 2020: 11108-11117.

[18] [18] Armeni I, Sener O, Zamir A R, et al. 3D semantic parsing of large-scale indoor spaces[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: CVF, 2016: 1534-1543.

[19] [19] HACKEL T, SAVINOV N, LADICKY L, et al. Semantic3D. net: a new large-scale point cloud classification benchmark[EB/OL]. (2017-04-12). https://arxiv.org/abs/1704.03847.

[20] [20] Zhao H S, Jiang L, Fu C W, et al. PointWeb: enhancing local neighborhood features for point cloud processing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: CVF, 2019: 5565-5573.

[21] [21] Wang Y, Sun Y B, Liu Z W, et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 2019, 38(5): 146.

[22] [22] Guo M H, Cai J X, Liu Z N, et al. PCT: point cloud transformer[J]. Computational Visual Media, 2021, 7(2): 187-199.

[23] [23] Shuai H, Xu X, Liu Q S. Backward attentive fusing network with local aggregation classifier for 3D point cloud semantic segmentation[J]. IEEE Transactions on Image Processing, 2021, 30: 4973-4984.

[24] [24] Boulch A, Guerry J, Le Saux B, et al. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks[J]. Computers & Graphics, 2018, 71: 189-198.

[25] [25] MA Y N, GUO Y L, LIU H, et al. Global context reasoning for semantic segmentation of 3D point clouds[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Snowmass: CVF, 2020: 2931-2940.

[26] [26] Qiu S, Anwar S, Barnes N. Semantic segmentation for real point cloud scenes via bilateral augmentation and adaptive fusion[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: CVF, 2021: 1757-1767.

[27] [27] Zeng Z Y, Xu Y Y, Xie Z, et al. LEARD-Net: semantic segmentation for large-scale point cloud scene[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112: 102953.