Optics and Precision Engineering, Volume. 32, Issue 18, 2823(2024)
3D point cloud classification and segmentation based on dual attention and weighted dynamic graph convolution
[1] H SU, S MAJI, E KALOGERAKIS et al. Multi-view convolutional neural networks for 3D shape recognition, 945-953(2015).
[2] D MATURANA, S SCHERER. Voxnet: a 3D convolutional neural network for real-time object recognition, 922-928(2015).
[3] G RIEGLER, A O ULUSOY, A GEIGER. OctNet: learning deep 3D representations at high resolutions, 6620-6629(2017).
[4] P S WANG, Y LIU, Y X GUO et al. O-CNN. ACM Transactions on Graphics, 36, 1-11(2017).
[5] R Q CHARLES, S HAO, K C MO et al. PointNet: deep learning on point sets for 3D classification and segmentation, 77-85(2017).
[6] C R QI, L YI, H SU et al. Pointnet++: deep hierar-chical feature learning on point sets in a metric spa-ce. Advances in Neural Information Processing Systems, 30, 5099-5108(2017).
[7] Y WANG, Y B SUN, Z W LIU et al. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 38, 1-12(2019).
[8] Q Y HU, B YANG, L H XIE et al. RandLA-net: efficient semantic segmentation of large-scale point clouds, 11105-11114(2020).
[9] H THOMAS, C R QI, J E DESCHAUD et al. KPConv: flexible and deformable convolution for point clouds, 6410-6419(2019).
[10] [10] 杨军, 王连甲. 结合位置关系卷积与深度残差网络的三维点云识别与分割[J]. 西安交通大学学报, 2023, 57(5): 182-193.YANGJ, WANGL J. Recognition and segmentation of 3D point cloud through positional relation convolution in combination with deep residual network[J]. Journal of Xi'an Jiaotong University, 2023, 57(5): 182-193.(in Chinese)
[11] H S ZHAO, L JIANG, C W FU et al. PointWeb: enhancing local neighborhood features for point cloud processing, 5560-5568(2019).
[12] [12] 王江安, 何娇, 庞大为. 基于动态图卷积网络的点云分类和分割网络[J]. 激光与光电子学进展, 2021, 58(12): 1215008.WANGJ A, HEJ, PANGD W. Point cloud classification and segmentation network based on dynamic graph convolutional network[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1215008.(in Chinese)
[13] [13] 沈露, 杨家志, 周国清, 等. 集自注意力与边卷积的点云分类分割模型[J]. 计算机工程与应用, 2023, 59(19): 106-113.SHENL, YANGJ Z, ZHOUG Q, et al. Point cloud classification segmentation model based on self-attention and edge convolution[J]. Computer Engineering and Applications, 2023, 59(19): 106-113.(in Chinese)
[14] [14] 杨军,李博赞. 基于自注意力特征融合组卷积神经网络的三维点云语义分割[J].光学 精密工程,2022,30(7):840-853. doi: 10.37188/OPE.20223007.0840YANGJ,LIB Z. Semantic segmentation of 3D point cloud based on self-attention feature fusion groupco-nvolutional neural network[J]. Opt. Precision Eng.,2022,30(7):840-853.(in Chinese). doi: 10.37188/OPE.20223007.0840
[15] Y J CHEN, Z L YANG, X W ZHENG et al. Pointformer: a dual perception attention-based network for point cloud classification, 432-449(2023).
[16] Z L YANG, Y J CHEN, X W ZHENG et al. Conditional GAN for point cloud generation, 117-133(2023).
[17] J HU, L SHEN, G SUN. Squeeze-and-excitation Networks, 7132-7141(2018).
[18] Z R WU, S R SONG, A KHOSLA et al. 3D ShapeNets: a deep representation for volumetric shapes, 1912-1920(2015).
[19] L YI, V G KIM, D CEYLAN et al. A scalable active framework for region annotation in 3D shape collections. ACM Transactions on Graphics, 35, 1-12(2016).
[20] [20] 兰红, 陈浩, 张蒲芬. 集图卷积和三维方向卷积的点云分类分割模型[J]. 计算机工程与应用, 2023, 59(8): 182-191. doi: 10.3778/j.issn.1002-8331.2112-0412LANH, CHENH, ZHANGP F. Point cloud classification and segmentation model based on graph convolution and 3D direction convolution[J]. Computer Engineering and Applications, 2023, 59(8): 182-191.(in Chinese). doi: 10.3778/j.issn.1002-8331.2112-0412
[21] H S ZHAO, L JIANG, J Y JIA et al. Point transformer, 16239-16248(2021).
[22] [22] 梁正友, 蔡俊民, 孙宇, 等. 结合残差动态图卷积与特征强化的点云分类[J]. 广西师范大学学报(自然科学版), 2023, 41(5): 37-48.LIANGZ Y, CAIJ M, SUNY, et al. Point cloud classification based on residual dynamic graph convolution and feature enhancement[J]. Journal of Guangxi Normal University (Natural Science Edition), 2023, 41(5): 37-48.(in Chinese)
[23] W X WU, Z A QI, F X LI. PointConv: deep convolutional networks on 3D point clouds, 9613-9622(2019).
[24] X YAN, C D ZHENG, Z LI et al. PointASNL: robust point clouds processing using nonlocal neural networks with adaptive sampling, 5588-5597(2020).
[25] Y F XU, T Q FAN, M Y XU et al. SpiderCNN: deep learning on point sets with parameterized convolutional filters, 90-105(2018).
[26] X F HAN, Y F JIN, H X CHENG et al. Dual transformer for point cloud analysis. IEEE Transactions on Multimedia, 25, 5638-5648(2022).
[27] Z H LIN, S Y HUANG, Y C F WANG. Convolution in The Cloud: learning deformable kernels in 3D graph convolution networks for point cloud analysis, 1797-1806(2020).
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Jian XIAO, Xiaohong WANG, Wei LI, Yifei YANG, Ji LUO. 3D point cloud classification and segmentation based on dual attention and weighted dynamic graph convolution[J]. Optics and Precision Engineering, 2024, 32(18): 2823
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Received: Jan. 25, 2024
Accepted: --
Published Online: Nov. 18, 2024
The Author Email: WANG Xiaohong (xhwang@gzu.edu.cn)