[1] [1] Guo Y L, Wang H Y, Hu Q Y, et al. Deep learning for 3D point clouds: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(12): 4338-4364.

[2] [2] Bello S A, Yu S, Wang C, et al. Review: deep learning on 3D point clouds[J]. Remote Sensing, 2020, 12(11): 1729.

[3] [3] Lu D N, Xie Q, Wei M Q, et al. Transformers in 3d point clouds: A survey[DB/OL]. (2022-05-16). https://arxiv.org/abs/2205.07417.

[4] [4] Wu W C, Xie Z, Xu Y Y, et al. Point projection network: a multi-view-based point completion network with encoder-decoder architecture[J]. Remote Sensing, 2021, 13(23): 4917.

[5] [5] Chen X Z, Ma H M, Wan J, et al. Multi-view 3D object detection network for autonomous driving[C]//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. Honolulu: CVF, 2017: 1907-1915.

[6] [6] Yu T, Meng J J, Yuan J S. Multi-view harmonized bilinear network for 3d object recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: CVF, 2018: 186-194.

[7] [7] Le T, Duan Y. PointGrid: a deep network for 3D shape understanding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: CVF, 2018: 9204-9214.

[8] [8] Graham B, Engelcke M, Van Der Maaten L. 3D semantic segmentation with submanifold sparse convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: CVF, 2018: 9224-9232.

[9] [9] 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.

[10] [10] Tchapmi L, Choy C, Armeni I, et al. SEGCloud: semantic segmentation of 3D point clouds[C]//2017 International Conference on 3D Vision (3DV). Qingdao: IEEE, 2017: 537-547.

[11] [11] 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. Honolulu: CVF, 2017: 652-660.

[12] [12] Qi C R, Yi L, Su H, et al. Pointnet++: deep hierarchical feature learning on point sets in a metric space[C]//Advances in Neural Information Processing Systems 30 (NeurIPS 2017). Red Hook, NY: Curran Associates, Inc., 2017: 5099-5108.

[13] [13] Li Y Y, Bu R, Sun M C, et al. Pointcnn: convolution on x-transformed points[C]//Advances in Neural Information Processing Systems 31 (NeurIPS 2018). Red Hook, NY: Curran Associates, Inc., 2018: 820-830.

[14] [14] 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.

[15] [15] Landrieu L, Simonovsky M. Large-Scale point cloud semantic segmentation with superpoint graphs[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: CVF, 2018: 4558-4567.

[16] [16] Wang S L, Suo S, Ma W C, et al. Deep parametric continuous convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: CVF, 2018: 2589-2597.

[17] [17] 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.

[18] [18] Guo M H, Cai J X, Liu Z N, et al. PCT: point cloud transformer[J]. Computational Visual Media, 2021, 7(2): 187-199.

[19] [19] Yan X, Zheng C D, 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. Seattle: CVF, 2020: 5589-5598.

[20] [20] 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. Seattle: CVF, 2020: 11108-11117.

[21] [21] Wang L, Huang Y C, Hou Y L, et al. Graph attention convolution for point cloud semantic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: CVF, 2019: 10296-10305.

[22] [22] Fan S Q, Dong Q L, Zhu F H, et al. SCF-Net: learning spatial contextual features for Large-Scale point cloud segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: CVF, 2021: 14504-14513.

[23] [23] Chen C, Wang Y S, Chen H H, et al. GeoSegNet: point cloud semantic segmentation via geometric encoder-decoder modeling[J]. The Visual Computer, 2024, 40(8): 5107-5121.

[24] [24] 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. Nashville: CVF, 2021: 1757-1767.

[25] [25] Zhao H S, Jiang L, Jia J Y, et al. Point transformer[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: CVF, 2021: 16259-16268.

[26] [26] Qian G, Hammoud H, Li G, et al. Assanet: an anisotropic separable set abstraction for efficient point cloud representation learning[C]//Advances in Neural Information Processing Systems 34. Red Hook, NY: Curran Associates, Inc., 2021: 28119-28130.

[27] [27] Liu Z, Mao H Z, Wu C Y, et al. A ConvNet for the 2020s[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: CVF, 2022: 11976-11986.

[28] [28] 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. Seoul, Korea (South): CVF, 2019: 6411-6420.