• Journal of Terahertz Science and Electronic Information Technology
  • Vol. 21, Issue 6, 838 (2023)
HAN Yifei1、2、*, LIU Yue1、3, ZHENG Fu1, WANG Yanqiu1, and SUN Zhibin1、2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.11805/tkyda2020610 Cite this Article
    HAN Yifei, LIU Yue, ZHENG Fu, WANG Yanqiu, SUN Zhibin. Iterative Closest Point registration algorithm based on intensity feature matching of TOF point cloud[J]. Journal of Terahertz Science and Electronic Information Technology , 2023, 21(6): 838 Copy Citation Text show less
    References

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    [7] OUYANG Daoshan, FENG Hsiyung. On the normal vector estimation for point cloud data from smooth surfaces[J]. Computer-Aided Design, 2005,37(10):1071-1079.

    [8] CHETVERIKOV D, SVIRKO D, STEPANOV D, et al. The trimmed iterative closest point algorithm[C]// 2002 International Conference on Pattern Recognition. Quebec City,QC,Canada:IEEE, 2002:545-548.

    [9] QI C R, SU H, MO K, et al. PointNet: deep learning on point sets for 3D classification and segmentation[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu,HI,USA:IEEE, 2017:77-85.

    [10] WANG Y,SOLOMON J M. Deep closest point:learning representations for point cloud registration[C]// IEEE/CVF International Conference on Computer Vision(ICCV). Seoul,Korea(South):IEEE, 2019:3522-3531.

    [15] BIBER P, STRASSER W. The normal distributions transform: a new approach to laser scan matching[C]// Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2003). Las Vegas,NV,USA:IEEE, 2003:2743-2748.

    HAN Yifei, LIU Yue, ZHENG Fu, WANG Yanqiu, SUN Zhibin. Iterative Closest Point registration algorithm based on intensity feature matching of TOF point cloud[J]. Journal of Terahertz Science and Electronic Information Technology , 2023, 21(6): 838
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