[1] [1] Guo Y, Wang H, Hu Q, et al. Deep learning for 3d point clouds: A survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 43(12): 4338-4364.

[2] [2] 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. 2019: 6411-6420.

[3] [3] Hu Q, Yang B, Xie L, 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. 2020: 11108-11117.

[4] [4] Yan X, Zheng C, Li Z, et al. Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 5589-5598.

[5] [5] Zhao H, Jiang L, Jia J, et al. Point transformer[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 16259-16268.

[6] [6] Zhou Y, Tuzel O. Voxelnet: End-to-end learning for point cloud based 3d object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4490-4499.

[7] [7] Meng H Y, Gao L, Lai Y K, et al. Vv-net: Voxel vae net with group convolutions for point cloud segmentation[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 8500-8508.

[8] [8] Wang C, Cheng M, Sohel F, et al. NormalNet: A voxel-based CNN for 3D object classification and retrieval[J]. Neurocomputing, 2019, 323: 139-147.

[9] [9] Xu Y, Tong X, Stilla U. Voxel-based representation of 3D point clouds: Methods, applications, and its potential use in the construction industry[J]. Automation in Construction, 2021, 126: 103675.

[10] [10] Zhou H, Zhu X, Song X, et al. Cylinder3d: An effective 3d framework for driving-scene lidar semantic segmentation[J]. arXiv preprint arXiv: 2008.01550, 2020.

[11] [11] Zhang Y, Zhou Z, David P, et al. Polarnet: An improved grid representation for online lidar point clouds semantic segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 9601-9610.

[12] [12] Wu B, Wan A, Yue X, et al. Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 1887-1893.

[13] [13] Wu B, Zhou X, Zhao S, et al. Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 4376-4382.

[14] [14] Milioto A, Vizzo I, Behley J, et al. Rangenet++: Fast and accurate lidar semantic segmentation[C]//2019 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2019: 4213-4220.

[15] [15] Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2019: 1314-1324.

[16] [16] Shi W, Caballero J, Huszr F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1874-1883.

[17] [17] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[J]. arXiv preprint arXiv: 1511.07122, 2015.

[18] [18] Wang P, Chen P, Yuan Y, et al. Understanding convolution for semantic segmentation[C]//2018 IEEE winter conference on applications of computer vision (WACV). Ieee, 2018: 1451-1460.

[19] [19] Berman M, Triki A R, Blaschko M B. The lovsz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4413-4421.

[20] [20] Behley J, Garbade M, Milioto A, et al. Semantickitti: A dataset for semantic scene understanding of lidar sequences[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 9297-9307.