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Laser & Optoelectronics Progress, Volume. 62, Issue 10, 1015006(2025)

Lightweight Point Cloud Classification Model Based on Offset Attention Mechanism

Leicheng Yang1,2, Yuhong Du1,2、*, and Guangyu Dong1,2
Author Affiliations
  • 1School of Mechanical Engineering, Tiangong University, Tianjin 300387, China
  • 2Key Laboratory of Advanced Mechatronics Equipment Technology, Tianjin 300387, China
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    [1] Peng K, Rao Y H, Xiao Y J et al. Point cloud target fusion segmentation algorithm in binocular visual spatial positioning system[J]. Journal of Chinese Inertial Technology, 31, 777-782, 789(2023).

    [2] Miao J Q, Wang H T, Tian P G. Airborne light detection and ranging point cloud classification via graph convolution and PointNet integration[J]. Laser & Optoelectronics Progress, 59, 2228003(2022).

    [3] Chen J D, Kira Z, Cho Y K. Deep learning approach to point cloud scene understanding for automated scan to 3D reconstruction[J]. Journal of Computing in Civil Engineering, 33, 04019027(2019).

    [4] Su H, Maji S, Kalogerakis E et al. Multi-view convolutional neural networks for 3D shape recognition[C], 945-953(2015).

    [5] Wei X, Yu R X, Sun J. View-GCN: view-based graph convolutional network for 3D shape analysis[C], 1847-1856(2020).

    [6] Zhou Y, Tuzel O. VoxelNet: end-to-end learning for point cloud based 3D object detection[C], 4490-4499(2018).

    [7] Choy C, Gwak J, Savarese S. 4D spatio-temporal ConvNets: minkowski convolutional neural networks[C], 3070-3079(2019).

    [8] Wen P, Cheng Y L, Yu W S. Point cloud classification methods based on deep learning: a review[J]. Laser & Optoelectronics Progress, 58, 1600003(2021).

    [9] 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).

    [10] Charles R Q, Yi L, Hao S et al. Pointnet++: deep hierarchical feature learning on point sets in a metric space[C], 5099-5108(2017).

    [11] Xu J, Liu H, Shen Y et al. Point clouds classification and segmentation for nursery trees based on improved PointNet++ model[J]. Chinese Journal of Lasers, 51, 0810001(2024).

    [12] Qian G C, Li Y C, Peng H W et al. PointNeXt: revisiting PointNet++ with improved training and scaling strategies[EB/OL]. https://arxiv.org/abs/2206.04670

    [13] Ma X, Qin C, You H X et al. Rethinking network design and local geometry in point cloud: a simple residual MLP framework[EB/OL]. https://doi.org/10.48550/arXiv.2202.07123

    [14] Zhang R R, Wang L H, Wang Y L et al. Parameter is not all you need: starting from non-parametric networks for 3D point cloud analysis[EB/OL]. https://doi.org/10.48550/arXiv.2303.08134

    [15] Zhao H S, Jiang L, Jia J Y et al. Point transformer[C], 16239-16248(2021).

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

    [17] Tian S, Song L, Zhao K L. Point cloud classification based on offset attention mechanism and multi-feature fusion[J]. Journal of South China University of Technology (Natural Science Edition), 52, 100-109(2024).

    [18] Wu Z R, Song S R, Khosla A et al. 3D ShapeNets: a deep representation for volumetric shapes[C], 1912-1920(2015).

    [19] Liu Y H, Tian B, Lv Y S et al. Point cloud classification using content-based transformer via clustering in feature space[J]. IEEE/CAA Journal of Automatica Sinica, 11, 231-239(2024).

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

    [21] 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).

    [22] Zhang K G, Hao M, Wang J et al. Linked dynamic graph CNN: learning on point cloud via linking hierarchical features[EB/OL]. https://doi.org/10.48550/arXiv.1904.10014

    [23] Chen C, Fragonara L Z, Tsourdos A. GAPointNet: graph attention based point neural network for exploiting local feature of point cloud[J]. Neurocomputing, 438, 122-132(2021).

    [24] Tancik M, Srinivasan P P, Mildenhall B et al. Fourier features let networks learn high frequency functions in low dimensional domains[EB/OL]. https://arxiv.org/abs/2006.10739v1

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    Leicheng Yang, Yuhong Du, Guangyu Dong. Lightweight Point Cloud Classification Model Based on Offset Attention Mechanism[J]. Laser & Optoelectronics Progress, 2025, 62(10): 1015006

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    Paper Information

    Category: Machine Vision

    Received: Sep. 30, 2024

    Accepted: Nov. 7, 2024

    Published Online: May. 9, 2025

    The Author Email: Yuhong Du (16629268205@163.com)

    DOI:10.3788/LOP242058

    CSTR:32186.14.LOP242058

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology