[7] Qi C R, Su H, Mo K, 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, 2017: 652-660.
[8] 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.
[10] Armeni I, Sax S, Zamir A R, et al. Joint 2d-3d-semantic data for indoor scene understanding[J]. arXiv preprint arXiv: 1702.01105, 2017.
[11] Dai A, Chang A X, Savva M, et al. Scannet: Richly-annotated 3d reconstructions of indoor scenes[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2017: 5828-5839.
[13] 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.
[14] Lawin F J, Danelljan M, Tosteberg P, et al. Deep projective 3D semantic segmentation[C]//Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I 17, 2017: 95-107.
[15] Kundu A, Yin X, Fathi A, et al. Virtual multi-view fusion for 3d semantic segmentation[C]//Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIV 16, 2020: 518-535.
[16] Robert D, Vallet B, Landrieu L. Learning multi-view aggregation in the wild for large-scale 3d semantic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 5575-5584.
[17] Hu W, Zhao H, Jiang L, et al. Bidirectional projection network for cross dimension scene understanding[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 14373-14382.
[18] Hu Z, Bai X, Shang J, et al. Vmnet: Voxel-mesh network for geodesic - aware 3d semantic segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 15488-15498.
[19] Zhang F, Fang J, Wah B, et al. Deep fusionnet for point cloud semantic segmentation[C]//Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIV 16, 2020: 644-663.
[20] Fang Z, Xiong B, Liu F. Sparse point-voxel aggregation network for efficient point cloud semantic segmentation[J]. IET Computer Vision, 2022, 16(7): 644-654.
[21] Zhang C, Wan H, Shen X, et al. PVT: Point-voxel transformer for point cloud learning[J]. International Journal of Intelligent Systems, 2022, 37(12): 11985-12008.
[22] 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.
[23] Qi C R, Yi L, Su H, et al. Pointnet++: Deep hierarchical feature learning on point sets in a metric space[J]. Advances in neural information processing systems, 2017, 30: 5099-5108.
[24] Ran H, Zhuo W, Liu J, et al. Learning inner-group relations on point clouds[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 15477-15487.
[25] Qian G, Li Y, Peng H, et al. Pointnext: Revisiting pointnet ++ with improved training and scaling strategies[J]. Advances in Neural Information Processing Systems, 2022, 35: 23192-23204.
[26] Yu X, Tang L, Rao Y, et al. Point-bert: Pre-training 3d point cloud transformers with masked point modeling[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022: 19313-19322.
[27] Lin H, Zheng X, Li L, et al. Meta architecture for point cloud analysis[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 17682-17691.
[28] Deng X, Zhang W, Ding Q, et al. Pointvector: a vector representation in point cloud analysis[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 9455-9465.
[29] Choe J, Park C, Rameau F, et al. Pointmixer: Mlp-mixer for point cloud understanding[C]//European Conference on Computer Vision, 2022: 620-640.
[30] Choe J, Park C, Rameau F, et al. Pointmixer: Mlp-mixer for point cloud understanding[C]//Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXVII, 2022: 620-640.
[31] Zhao H, Jiang L, Jia J, et al. Point transformer[C]//Proceedings of the IEEE/CVF international conference on computer vision, 2021: 16259-16268.
[32] Wu X, Lao Y, Jiang L, et al. Point transformer v2: Grouped vector attention and partition-based pooling[J]. Advances in Neural Information Processing Systems, 2022, 35: 33330-33342.
[33] Park J, Lee S, Kim S, et al. Self-positioning point-based transformer for point cloud understanding[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 21814-21823.
[34] Lai X, Liu J, Jiang L, et al. Stratified transformer for 3d point cloud segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 8500-8509.
[35] Yang YQ, Guo YX, Xiong JY, et al. Swin3d: A pretrained transformer backbone for 3d indoor scene understanding[J]. arXiv preprint arXiv: 2304.06906, 2023.
[36] Wang PS. Octformer: Octree - based transformers for 3d point clouds[J]. ACM Transactions on Graphics (TOG), 2023, 42(4): 1-11.
[37] Boulch A. ConvPoint: Continuous convolutions for point cloud processing[J]. Computers & Graphics, 2020, 88: 24-34.
[38] 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.
[39] Li Y, Li X, Zhang Z, et al. DenseKPNET: Dense kernel point convolutional neural networks for point cloud semantic segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-13.
[40] Xu M, Ding R, Zhao H, et al. Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 3173-3182.
[41] Li Y, Fan C, Wang X, et al. Spnet: Multi-shell kernel convolution for point cloud semantic segmentation[C]//Advances in Visual Computing: 16th International Symposium, ISVC 2021, Virtual Event, October 4-6, 2021, Proceedings, Part I, 2021: 366-378.
[43] Luo N, Yu H, Huo Z, et al. KVGCN: A KNN searching and VLAD combined graph convolutional network for point cloud segmentation[J]. Remote Sensing, 2021, 13(5): 1003.
[44] Lin ZH, Huang SY, Wang Y-C F. Learning of 3d graph convolution networks for point cloud analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(8): 4212-4224.
[45] Wang Y, Zhang Z, Zhong R, et al. Densely connected graph convolutional network for joint semantic and instance segmentation of indoor point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 182: 67-77.
[46] Du Z, Ye H, Cao F. A novel local-global graph convolutional method for point cloud semantic segmentation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 159:98-105.
[47] Chen L, Zhang Q. DDGCN: graph convolution network based on direction and distance for point cloud learning[J]. The Visual Computer, 2023, 39(3): 863-873.
[48] Lu Q, Chen C, Xie W, et al. PointNGCNN: Deep convolutional networks on 3D point clouds with neighborhood graph filters[J]. Computers & Graphics, 2020, 86: 42-51.
[49] Wei J, Lin G, Yap K-H, et al. Multi-path region mining for weakly supervised 3d semantic segmentation on point clouds[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020: 4384-4393.
[50] Li M, Xie Y, Shen Y, et al. Hybridcr: Weakly-supervised 3d point cloud semantic segmentation via hybrid contrastive regularization[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022: 14930-14939.
[51] Lee M S, Yang S W, Han S W. Gaia: Graphical information gain based attention network for weakly supervised point cloud semantic segmentation[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023: 582-591.
[52] Tang L, Zhan Y, Chen Z, et al. Contrastive boundary learning for point cloud segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 8489-8499.
[53] Wang Q, Shi S, Li J, et al. Window normalization: enhancing point cloud understanding by unifying inconsistent point densities[J]. arXiv preprint arXiv: 2212.02287, 2022.
[54] Nekrasov A, Schult J, Litany O, et al. Mix3d: Out-of-context data augmentation for 3d scenes[C]//2021 international conference on 3d vision (3dv), 2021: 116-125.
[55] Xie S, Gu J, Guo D, et al. Pointcontrast: Unsupervised pretraining for 3d point cloud understanding[C]//Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23 - 28, 2020, Proceedings, Part III 16, 2020: 574-591.