Optics and Precision Engineering, Volume. 30, Issue 24, 3210(2022)

Robust point cloud registration of terra-cotta warriors based on dynamic graph attention mechanism

Linqi HAI... Guohua GENG, Xing YANG, Kang LI and Haibo ZHANG* |Show fewer author(s)
Author Affiliations
  • School of Information Science and Technology, Northwest University, Xi’an710127, China
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    References(40)

    [1] [1] 1王宾, 刘林, 侯榆青, 等. 应用改进迭代最近点方法的三维心脏点云配准[J]. 光学 精密工程, 2020, 28(2): 474-484. doi: 10.3788/OPE.20202802.0474WANGB, LIUL, HOUY Q, et al. Three-dimensional cardiac point cloud registration by improved iterative closest point method[J]. Opt. Precision Eng., 2020, 28(2): 474-484.(in Chinese). doi: 10.3788/OPE.20202802.0474

    [2] [2] 2李晓燕. 低重叠率三维点云配准技术研究[D]. 太原:中北大学, 2021.LIX Y. Reaserch on 3D Point Cloud Registration Technology with Low Overlap[D]. Taiyuan: North University of China. School of Data Science and Technology, 2021. (in Chinese)

    [4] [4] 4张顺利, 徐艳芝, 周明全, 等. 基于自适应邻域匹配的点云配准方法[J]. 计算机学报, 2019, 42(9): 2114-2126. doi: 10.11897/SP.J.1016.2019.02114ZHANGSH L, XUY Z, ZHOUM Q, et al. Registration of point clouds based on matching of general adaptive neighborhood[J]. Chinese Journal of Computers, 2019, 42(9): 2114-2126.(in Chinese). doi: 10.11897/SP.J.1016.2019.02114

    [5] [5] 5吴庆华, 蔡琼捷思, 黎志昂, 等. 扩展高斯图像聚类的缺失点云配准[J]. 光学 精密工程, 2021, 29(5): 1199-1206. doi: 10.37188/OPE.20212905.1199WUQ H, CAIQ J S, LIZH A, et al. Registration of losing point cloud based on clustering extended Gaussian image[J]. Opt. Precision Eng., 2021, 29(5): 1199-1206.(in Chinese). doi: 10.37188/OPE.20212905.1199

    [6] [6] 6刘跃生, 陈新度, 吴磊, 等. 混合稀疏迭代最近点配准[J]. 光学 精密工程, 2021, 29(9): 2255-2267. doi: 10.37188/OPE.20212909.2255LIUY SH, CHENX D, WUL, et al. Sparse mixture iterative closest point registration[J]. Opt. Precision Eng., 2021, 29(9): 2255-2267.(in Chinese). doi: 10.37188/OPE.20212909.2255

    [7] F TOMBARI, S SALTI, L STEFANO. Unique signatures of histograms for local surface description, 356-369(2010).

    [8] F TOMBARI, S SALTI, L D STEFANO. Unique shape context for 3d data description, 57-62(2010).

    [9] Y L GUO, F SOHEL, M BENNAMOUN et al. Rotational projection statistics for 3D local surface description and object recognition. International Journal of Computer Vision, 105, 63-86(2013).

    [10] T BIRDAL, S ILIC. Point pair features based object detection and pose estimation revisited, 527-535(2015).

    [11] R B RUSU, N BLODOW, M BEETZ. Fast point feature histograms (FPFH) for 3D registration, 3212-3217(2009).

    [12] H CHEN, B BHANU. 3D free-form object recognition in range images using local surface patches. Pattern Recognition Letters, 28, 1252-1262(2007).

    [13] Z J YEW, G H LEE. RPM-net: robust point matching using learned features, 11821-11830(2020).

    [15] A VASWANI, N SHAZEER, N PARMAR et al. Attention is all you need, 5998-6008(2017).

    [16] Z GOJCIC, C F ZHOU, J D WEGNER et al. The perfect match: 3D point cloud matching with smoothed densities, 5540-5549(2019).

    [17] C CHOY, J PARK, V KOLTUN. Fully convolutional geometric features, 8957-8965(2019).

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

    [20] M FISCHLER, R BOLLES. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM, 24, 381-395(1981).

    [21] V SARODE, A DHAGAT, R A SRIVATSAN et al. MaskNet: a fully-convolutional network to estimate inlier points, 1029-1038(2020).

    [22] H XU, S C LIU, G F WANG et al. OMNet: learning overlapping mask for partial-to-partial point cloud registration, 3112-3121(2021).

    [23] X T LIU, B D KILLEEN, A SINHA et al. Neighborhood normalization for robust geometric feature learning, 13044-13053(2021).

    [24] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(2016).

    [25] O RONNEBERGER, P FISCHER, T BROX. U-net: convolutional networks for biomedical image segmentation, 234-24(2015).

    [26] C CHOY, W DONG, V KOLTUN. Deep global registration, 2511-2520(2020).

    [27] S Y HUANG, Z GOJCIC, M USVYATSOV et al. PREDATOR: registration of 3D point clouds with low overlap, 4265-4274(2021).

    [28] C SZEGEDY, W LIU, Y Q JIA et al. Going deeper with convolutions, 1-9(2015).

    [29] 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).

    [30] C H SHI, X CHEN, K H HUANG et al. Keypoint matching for point cloud registration using multiplex dynamic graph attention networks. IEEE Robotics and Automation Letters, 6, 8221-8228(2021).

    [31] P E SARLIN, D DETONE, T MALISIEWICZ et al. SuperGlue: learning feature matching with graph neural networks, 4937-4946(2020).

    [32] Y Q XIE, S LI, C YANG et al. When do GNNs work: understanding and improving neighborhood aggregation, 11, 2020(2020).

    [33] [33] 33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 group convolutional neural network[J]. Opt. Precision Eng., 2022, 30(7): 840-853.(in Chinese). doi: 10.37188/OPE.20223007.0840

    [34] A ZENG, S R SONG, M NIEßNER et al. 3DMatch: learning local geometric descriptors from RGB-D reconstructions, 199-208(2017).

    [35] H W DENG, T BIRDAL, S ILIC. PPFNet: global context aware local features for robust 3D point matching, 195-205(2018).

    [36] G ELBAZ, T AVRAHAM, A FISCHER. 3D point cloud registration for localization using a deep neural network auto-encoder, 2472-2481(2017).

    [37] Y X MA, Y L GUO, J ZHAO et al. Fast and accurate registration of structured point clouds with small overlaps, 643-651(2016).

    [38] H YANG, J N SHI, L CARLONE. TEASER: fast and certifiable point cloud registration. IEEE Transactions on Robotics, 37, 314-333(2021).

    [39] Y F SUN, C M CHENG, Y H ZHANG et al. Circle loss: a unified perspective of pair similarity optimization, 6397-6406(2020).

    [40] P J BESL, N D MCKAY. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-256(1992).

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    Linqi HAI, Guohua GENG, Xing YANG, Kang LI, Haibo ZHANG. Robust point cloud registration of terra-cotta warriors based on dynamic graph attention mechanism[J]. Optics and Precision Engineering, 2022, 30(24): 3210

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    Paper Information

    Category: Information Sciences

    Received: May. 12, 2022

    Accepted: --

    Published Online: Feb. 15, 2023

    The Author Email: ZHANG Haibo (zhanghb@nwu.edu.cn)

    DOI:10.37188/OPE.20223024.3210

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