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Acta Optica Sinica, Volume. 44, Issue 14, 1415001(2024)

Three-Dimensional Point Cloud Registration Network Based on Deep Interactive Multi-Scale Receptive Field Feature Learning

Han Zhou1, Xuchu Wang1,2、*, and Yue Yuan1
Author Affiliations
  • 1College of Optoelectronic Engineering, Chongqing University, Chongqing 400040, China
  • 2Key Laboratory of Optoelectronic Technology & Systems (Chongqing University), Ministry of Education, Chongqing 400040, China
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    [1] Yang Z X, Ye Q, Wang X F et al. Automated registration of cross-source and multi-temporal point clouds in urban areas[J]. Chinese Journal of Lasers, 50, 1010004(2023).

    [2] Deschaud J E. IMLS-SLAM: scan-to-model matching based on 3D data[C], 2018, 2480-2485(2018).

    [3] Aoki Y, Goforth H, Srivatsan R A et al. PointNetLK: robust & efficient point cloud registration using PointNet[C], 7156-7165(2019).

    [4] Wang Y, Solomon J. Deep closest point: learning representations for point cloud registration[C], 3522-3531(2019).

    [5] Wang Y, Solomon J M. PRNet: self-supervised learning for partial-to-partial registration[EB/OL]. http:∥arxiv.org/abs/1910.12240v2

    [6] Yew Z J, Lee G H. RPM-net: robust point matching using learned features[C], 11821-11830(2020).

    [7] Choy C, Dong W, Koltun V. Deep global registration[C], 2511-2520(2020).

    [8] Pais G D, Ramalingam S, Govindu V M et al. 3DRegNet: a deep neural network for 3D point registration[C], 7191-7201(2020).

    [9] Bai X Y, Luo Z X, Zhou L et al. PointDSC: robust point cloud registration using deep spatial consistency[C], 15854-15864(2021).

    [10] Fu K X, Luo J Z, Luo X Y et al. Robust point cloud registration framework based on deep graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 6183-6195(2023).

    [11] Zanfir A, Sminchisescu C. Deep learning of graph matching[C], 2684-2693(2018).

    [12] Huang S Y, Gojcic Z, Usvyatsov M et al. PREDATOR: registration of 3D point clouds with low overlap[C], 4265-4274(2021).

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

    [14] Zeng A, Song S R, Nießner M et al. 3DMatch: learning local geometric descriptors from RGB-D reconstructions[C], 199-208(2017).

    [15] Xu H, Liu S C, Wang G F et al. OMNet: learning overlapping mask for partial-to-partial point cloud registration[C], 3112-3121(2021).

    [16] Yew Z J, Lee G H. REGTR: end-to-end point cloud correspondences with transformers[C], 6667-6676(2022).

    [17] Gojcic Z, Zhou C F, Wegner J D et al. The perfect match: 3D point cloud matching with smoothed densities[C], 5540-5549(2019).

    [18] Bai X Y, Luo Z X, Zhou L et al. D3Feat: joint learning of dense detection and description of 3D local features[C], 6358-6366(2020).

    [19] Qin Z, Yu H, Wang C J et al. GeoTransformer: fast and robust point cloud registration with geometric transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 9806-9821(2023).

    [20] Yew Z J, Lee G H, Ferrari V, Hebert M, Sminchisescu C et al. 3DFeat-net: weakly supervised local 3D features for point cloud registration[M]. Computer vision-ECCV 2018, 11219, 630-646(2018).

    [21] Xu J B, Huang Y K, Wan Z Y et al. GLORN: strong generalization fully convolutional network for low-overlap point cloud registration[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5704814(2022).

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    Han Zhou, Xuchu Wang, Yue Yuan. Three-Dimensional Point Cloud Registration Network Based on Deep Interactive Multi-Scale Receptive Field Feature Learning[J]. Acta Optica Sinica, 2024, 44(14): 1415001

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    Paper Information

    Category: Machine Vision

    Received: Jan. 19, 2024

    Accepted: Apr. 15, 2024

    Published Online: Jul. 4, 2024

    The Author Email: Xuchu Wang (xcwang@cqu.edu.cn)

    DOI:10.3788/AOS240529

    CSTR:32393.14.AOS240529

    Topics

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