[1] Zhang H, Wang C S, Tian S W et al. Deep learning-based 3D point cloud classification: a systematic survey and outlook[J]. Displays, 79, 102456(2023).

[2] Ding Z F, Sun Y X, Xu S J et al. Recent advances and perspectives in deep learning techniques for 3D point cloud data processing[J]. Robotics, 12, 100(2023).

[3] Sohail S S, Himeur Y, Kheddar H et al. Advancing 3D point cloud understanding through deep transfer learning: a comprehensive survey[J]. Information Fusion, 113, 102601(2025).

[4] Feng Y F, Zhang Z Z, Zhao X B et al. GVCNN: group-view convolutional neural networks for 3D shape recognition[C], 264-272(2018).

[5] Hamdi A, Giancola S, Ghanem B. MVTN: multi-view transformation network for 3D shape recognition[C](2021).

[6] Maturana D, Scherer S. VoxNet: a 3D convolutional neural network for real-time object recognition[C], 922-928(2015).

[7] Meng H Y, Gao L, Lai Y K et al. VV-Net: voxel VAE net with group convolutions for point cloud segmentation[C], 8499-8507(2019).

[8] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C], 77-85(2017).

[9] Charles R Q, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C], 5105-5114(2017).

[10] Zhang C, Wang Z J, Wu H et al. Lidar point cloud segmentation model based on improved PointNet++[J]. Laser & Optoelectronics Progress, 61, 0411001(2024).

[11] Wu W X, Qi Z A, Li F X. PointConv: deep convolutional networks on 3D point clouds[C], 9613-9622(2019).

[12] Lu B, Liu Y W, Zhang Y H et al. Point cloud segmentation algorithm based on density awareness and self-attention mechanism[J]. Laser & Optoelectronics Progress, 61, 0811004(2024).

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

[14] Lin Z H, Huang S Y, Wang Y C F. Convolution in the cloud: learning deformable kernels in 3D graph convolution networks for point cloud analysis[C], 1797-1806(2020).

[15] Meng X H, Lu X Y, Ye H L et al. A new self-augment CNN for 3D point cloud classification and segmentation[J]. International Journal of Machine Learning and Cybernetics, 15, 807-818(2024).

[16] Lan H, Chen H, Zhang P F. Point cloud classification and segmentation model based on graph convolution and 3D direction convolution[J]. Computer Engineering and Applications, 59, 182-191(2023).

[17] Zhang R R, Wang L H, Guo Z Y et al. Parameter is not all you need: starting from non-parametric networks for 3D point cloud analysis[EB/OL]. https://arxiv.org/abs/2303.08134v2

[18] Wang B L, Huo Z Q. Research on feature extraction of 3D point cloud based on MANet[J]. Computer Engineering and Applications, 58, 267-275(2022).

[19] Hu M, Ye H L, Cao F L. Convolutional neural networks with hybrid weights for 3D point cloud classification[J]. Applied Intelligence, 51, 6983-6996(2021).

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

[21] Lu D N, Xie Q, Gao K et al. 3DCTN: 3D convolution-transformer network for point cloud classification[J]. IEEE Transactions on Intelligent Transportation Systems, 23, 24854-24865(2022).

[22] Atzmon M, Maron H, Lipman Y. Point convolutional neural networks by extension operators[EB/OL]. https://arxiv.org/abs/1803.10091v1

[23] Han X F, Jin Y F, Cheng H X et al. Dual transformer for point cloud analysis[J]. IEEE Transactions on Multimedia, 25, 5638-5648(2023).

[24] Zhang J L, Chen L J, Ouyang B et al. PointCutMix: regularization strategy for point cloud classification[J]. Neurocomputing, 505, 58-67(2022).

[25] Jing W P, Zhang W J, Li L H et al. AGNet: an attention-based graph network for point cloud classification and segmentation[J]. Remote Sensing, 14, 1036(2022).

[26] Xie S N, Liu S N, Chen Z Y et al. Attentional ShapeContextNet for point cloud recognition[C], 4606-4615(2018).

[27] Huang Q G, Wang W Y, Neumann U. Recurrent slice networks for 3D segmentation of point clouds[C], 2626-2635(2018).

[28] Gao J J, Lan J H, Wang B X et al. SDANet: spatial deep attention-based for point cloud classification and segmentation[J]. Machine Learning, 111, 1327-1348(2022).

[29] Cao J, Peng Y Q, Fan L K et al. 3D object detection based on voxel self-attention auxiliary networks[J]. Laser & Optoelectronics Progress, 61, 2415004(2024).

[30] Dong H X, An Y, Xie L R et al. Semantic segmentation of large‑scale laser point cloud in mines based on local feature enhancement[J]. Chinese Journal of Lasers, 51, 1710002(2024).

[31] Dai C G, Zhang Y J, Ji H L et al. Place recognition method based on feature fusion for LiDAR point clouds[J]. Acta Optica Sinica, 45, 0628002(2025).