[2] [2] Qi C R, Su H, Mo K, et al. PointNet: Deep learning on point sets for 3D classification and segmentation[C]// Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2017: 652-660.

[3] [3] Qi C R, Li Y, Hao S, et al. PointNet++: Deep hierarchical feature learning on point sets in a metric space[C]// Proc. of Conf. and Workshop on Neural Information Processing Systems(NIPS), 2017: 5105-5114.

[4] [4] Jiang M, Wu Y, Zhao T, et al. Pointsift: A siftlike network module for 3d pointcloud semantic segmentation[J]. ArXiv preprint, 2018, 1807: 00652.

[5] [5] Wang Y, Sun Y, Liu Z, et al. Dynamic graph CNN for learning on point clouds[J]. ArXiv preprint, 2019, 1801: 07829.

[6] [6] Hu Q, Yang B, Xie L, et al. RandLA-Net: Efficient semantic segmentation of large-scale point clouds[C]// Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2020: 11108-11117.

[7] [7] Zhao H, Jiang L, Jia J, et al. Point transformer[C]// Computer Vision and Pattern Recognition, 2021: 389-409.

[8] [8] Lai X, Liu J, Jiang L, et al. Stratified transformer for 3D point cloud segmentation[C]// Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2022: 8490-8499.

[10] [10] Liang Z, Yang M, Li H, et al. 3D instance embedding learning with a structure-aware loss function for point cloud segmentation[J]. IEEE Rob. Autom. Lett., 2020, 5(3): 4915-4922.

[11] [11] Armeni I, Sener O, Zamir A, et al. 3D semantic parsing of largescale indoor spaces[C]// IEEE Conf. on Computer Vision and Pattern Recognition, 2016: 1534-1543.

[12] [12] Wang X L, Liu S, Shen X Y, et al. Associatively segmenting instances and semantics in point clouds[C]// IEEE Conf. on Computer Vision and Pattern Recognition, 2019: 4096-4105.

[13] [13] Liu W Y, Wen Y, Yu Z, et al. Large-margin softmax loss for convolutional neural networks[C]// Inter. Conf. on Machine Learning, 2016: 7-18.

[14] [14] Xu M, Ding R, Zhao H, et al. Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds[C]// Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2021: 3173-3182.