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*
Author Affiliations
  • School of Information Science and Technology, Northwest University, Xi’an710127, China
  • show less
    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] TOMBARI F, SALTI S, STEFANO L. Unique signatures of histograms for local surface description[C], 356-369(2010).

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

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

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

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

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

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

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

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

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

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

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

    [22] 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).

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

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

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

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

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

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

    [29] WANG Y, SUN Y B, LIU Z W et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 38, 1-12(2019).

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

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

    [32] XIE Y Q, LI S, YANG C et al. When do GNNs work: understanding and improving neighborhood aggregation[C], 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] ZENG A, SONG S R, NIEßNER M et al. 3DMatch: learning local geometric descriptors from RGB-D reconstructions[C], 199-208(2017).

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

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

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

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

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

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

    Tools

    Get Citation

    Copy Citation Text

    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

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Information Sciences

    Received: May. 12, 2022

    Accepted: --

    Published Online: Feb. 15, 2023

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

    DOI:10.37188/OPE.20223024.3210

    Topics