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An Improved Algorithm of Precise Point Cloud Registration

Received: 28 July 2015    Accepted: 16 August 2015    Published: 2 September 2015
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Abstract

Basing on the application of digital protection of cultural sites, this paper presented a precise algorithm for the multi-resolution point cloud based on sequence iterative. Firstly, based on the synchronous scanning image, a rough registration of the adjacent point cloud can be obtained in an interactive way. Secondly, based on a normal-based algorithm of registration sphere center, the points of sphere surface are filtered from all scanned point cloud data according to the normal data points and the characteristic of viewpoint, and then the center of registration sphere can be calculated accurately and the initial registration of the adjacent point cloud can be obtained by setting Matching Registration Labels mode as the constraint condition. Finally, based on the 3D edge points of the adjacent point cloud from mahalanobis distance calculations, some overlapping images can be eliminated by the way of constantly optimizing the transition matrix in the iteration process. The engineering practice of the Small Wild Goose Pagoda in Tang Dynasty and the Ancient Tomb in Han Dynasty proves that the method is reliable and easy to design and implement and can effectively restrain the accumulative errors of sequence registrations.

Published in Pure and Applied Mathematics Journal (Volume 4, Issue 5-1)

This article belongs to the Special Issue Mathematical Aspects of Engineering Disciplines

DOI 10.11648/j.pamj.s.2015040501.19
Page(s) 46-50
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Digital Archaeology, Multi-View Point Cloud, 3D Registration, Iterative Algorithm

References
[1] P. J. Besl, N. D. McKay, “A method for registration of 3D shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2), pp 239–256.
[2] I. Stamos, M. Leordeann, “Automated feature-based range registration of urban scenes of large scale,” In: Proceedings of the IEEE Computer Vision and Pattern Recognition, Madison, 2003, 2, pp 18–20.
[3] K.S. Arun, T. S. Huang, S. D. Blostein, “Least square fitting of two 3D point sets,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, V9(5), pp 698–700.
[4] T. Masuda, N. Yokoya, “A robust method for registration and segmentation of multiple range images,” Computer Vision and Image Understanding, 1995, V61 (3), pp 295–307.
[5] A. Johnson, M. Hebert, “Surface registration by matching oriented points,” Proceedings of International Conference on Recent Advances in 3D Digital Imaging and Modeling[C], Ottawa, 1997, pp 121–128.
[6] LUO Xianbo, ZHONG Yuexian, LI Renju, “Data registration in 3-D scanning systems,” Tsinghua Univ (Sci &Tech), 2004, V44 (8), pp 1104–1106.
[7] Zhang Aiwa, Sun Weidong, Ge Chenghui, “Fast Gobal Registration of Multiple 3D Data Sets from Outdoor Large Scenes,” High Technology Letters, 2004, V14(6), pp 6–13.
[8] Zhu Yanjuan, Zhou Laishui, Zhang Liyan, “Registration of Scattered Cloud Data,” Journal of Computer-adied Design& Computer Graphics, 2006, 18 (4), pp 475–481.
[9] Wei Jiang, Xiong Bangshu, Feng Yan, “Normal-based Algorithm for Registration Sphere Center in Multiple Views,” Computer Engineering and Applications, 2005, (19), pp 15–17.
[10] Lu-shen Wu,Qing-jin Peng,“ Research and development of fringe projection-based methods in 3D shape reconstruction,” Journal of Zhejiang University SCIENCE A,2006(6), pp 1026 -1036
[11] Dror Aiger, Niloy J. Mitra, Daniel Cohen-Or., “4-points congruent sets for robust pairwise surface registration,”ACM Transactions on Graphics (TOG), 2008 (3).
[12] Da Silva J P Jr, Borges D L, de Barros Vidal F., “A dynamic approach for approximate pairwise alignment based on 4-points congruence sets of 3D points,” Proceedings of the 18th IEEE International Conference on Image Processing, 2011.
[13] Sofien Bouaziz, Andrea Tagliasacchi, Mark Pauly, “Sparse Iterative Closest Point,” Computer Graphics Forum, 2013 (5), pp 113–123.
Cite This Article
  • APA Style

    Jun Liu. (2015). An Improved Algorithm of Precise Point Cloud Registration. Pure and Applied Mathematics Journal, 4(5-1), 46-50. https://doi.org/10.11648/j.pamj.s.2015040501.19

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    Jun Liu. An Improved Algorithm of Precise Point Cloud Registration. Pure Appl. Math. J. 2015, 4(5-1), 46-50. doi: 10.11648/j.pamj.s.2015040501.19

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    AMA Style

    Jun Liu. An Improved Algorithm of Precise Point Cloud Registration. Pure Appl Math J. 2015;4(5-1):46-50. doi: 10.11648/j.pamj.s.2015040501.19

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  • @article{10.11648/j.pamj.s.2015040501.19,
      author = {Jun Liu},
      title = {An Improved Algorithm of Precise Point Cloud Registration},
      journal = {Pure and Applied Mathematics Journal},
      volume = {4},
      number = {5-1},
      pages = {46-50},
      doi = {10.11648/j.pamj.s.2015040501.19},
      url = {https://doi.org/10.11648/j.pamj.s.2015040501.19},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pamj.s.2015040501.19},
      abstract = {Basing on the application of digital protection of cultural sites, this paper presented a precise algorithm for the multi-resolution point cloud based on sequence iterative. Firstly, based on the synchronous scanning image, a rough registration of the adjacent point cloud can be obtained in an interactive way. Secondly, based on a normal-based algorithm of registration sphere center, the points of sphere surface are filtered from all scanned point cloud data according to the normal data points and the characteristic of viewpoint, and then the center of registration sphere can be calculated accurately and the initial registration of the adjacent point cloud can be obtained by setting Matching Registration Labels mode as the constraint condition. Finally, based on the 3D edge points of the adjacent point cloud from mahalanobis distance calculations, some overlapping images can be eliminated by the way of constantly optimizing the transition matrix in the iteration process. The engineering practice of the Small Wild Goose Pagoda in Tang Dynasty and the Ancient Tomb in Han Dynasty proves that the method is reliable and easy to design and implement and can effectively restrain the accumulative errors of sequence registrations.},
     year = {2015}
    }
    

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    AB  - Basing on the application of digital protection of cultural sites, this paper presented a precise algorithm for the multi-resolution point cloud based on sequence iterative. Firstly, based on the synchronous scanning image, a rough registration of the adjacent point cloud can be obtained in an interactive way. Secondly, based on a normal-based algorithm of registration sphere center, the points of sphere surface are filtered from all scanned point cloud data according to the normal data points and the characteristic of viewpoint, and then the center of registration sphere can be calculated accurately and the initial registration of the adjacent point cloud can be obtained by setting Matching Registration Labels mode as the constraint condition. Finally, based on the 3D edge points of the adjacent point cloud from mahalanobis distance calculations, some overlapping images can be eliminated by the way of constantly optimizing the transition matrix in the iteration process. The engineering practice of the Small Wild Goose Pagoda in Tang Dynasty and the Ancient Tomb in Han Dynasty proves that the method is reliable and easy to design and implement and can effectively restrain the accumulative errors of sequence registrations.
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Author Information
  • Department of Mathematics and Information Science, Weinan Normal University, Weinan Shaanxi, P. R. China; Institute of Visualization Technology, Northwest University, Xi’an Shaanxi, P. R. China

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