• Chinese Journal of Lasers
  • Vol. 50, Issue 10, 1010004 (2023)
Zexin Yang1、2, Qin Ye1、*, Xufei Wang3, and Ravi Peters2
Author Affiliations
  • 1College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
  • 23D Geoinformation Research Group, Delft University of Technology, Delft 2628 BL, the Netherlands
  • 3Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, China
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    DOI: 10.3788/CJL221360 Cite this Article Set citation alerts
    Zexin Yang, Qin Ye, Xufei Wang, Ravi Peters. Automated Registration of Cross‑Source and Multi‑Temporal Point Clouds in Urban Areas[J]. Chinese Journal of Lasers, 2023, 50(10): 1010004 Copy Citation Text show less
    References

    [1] Zhu Q, Li S M, Hu H et al. Multiple point clouds data fusion method for 3D city modeling[J]. Geomatics and Information Science of Wuhan University, 43, 1962-1971(2018).

    [2] Yan L, Ren D W, Xie H et al. Fusion method of LiDAR point cloud and dense matching point cloud[J]. Chinese Journal of Lasers, 49, 0910003(2022).

    [3] Yang Y L, Li J Y, Wang Y et al. Point cloud registration algorithm based on NDT and feature point detection[J]. Laser&Optoelectronics Progress, 59, 0810016(2022).

    [4] Wang C Y, Kong Y L, Chen J B. A method of building change detection based on multi-temporal LiDAR data[C], 327-332(2013).

    [5] Hu C M, Fei H J, Xia G F et al. High-precision registration of non-homologous point clouds in laser scanning and photogrammetry[J]. Laser & Optoelectronics Progress, 59, 2415007(2022).

    [6] 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).

    [7] Biber P, Strasser W. The normal distributions transform: a new approach to laser scan matching[C], 2743-2748(2003).

    [8] Chen S L, Nan L L, Xia R B et al. PLADE: a plane-based descriptor for point cloud registration with small overlap[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 2530-2540(2020).

    [9] Weinmann M, Weinmann M, Hinz S et al. Fast and automatic image-based registration of TLS data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 66, S62-S70(2011).

    [10] Theiler P W, Wegner J D, Schindler K. Keypoint-based 4-points congruent sets-automated marker-less registration of laser scans[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 96, 149-163(2014).

    [11] Zhang K, Yan J, Chen S C. Automatic construction of building footprints from airborne LIDAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 44, 2523-2533(2006).

    [12] Yang B S, Dong Z, Liang F et al. Automatic registration of large-scale urban scene point clouds based on semantic feature points[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 113, 43-58(2016).

    [13] Sampath A, Shan J. Building boundary tracing and regularization from airborne lidar point clouds[J]. Photogrammetric Engineering & Remote Sensing, 73, 805-812(2007).

    [14] Xu Y S, Boerner R, Yao W et al. Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 106-123(2019).

    [15] Rusu R B, Blodow N, Marton Z C et al. Aligning point cloud views using persistent feature histograms[C], 3384-3391(2008).

    [16] Aiger D, Mitra N J, Cohen-Or D. 4-points congruent sets for robust pairwise surface registration[J]. ACM Transactions on Graphic, 27, 85(2008).

    [17] Mellado N, Aiger D, Mitra N J. Super 4PCS fast global point cloud registration via smart indexing[J]. Computer Graphics Forum, 33, 205-215(2014).

    [18] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 24, 381-395(1981).

    [19] Zhang W M, Qi J B, Peng W et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing, 8, 501(2016).

    [20] Lafarge F, Mallet C. Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation[J]. International Journal of Computer Vision, 99, 69-85(2012).

    [21] Weinmann M, Jutzi B, Mallet C. Feature relevance assessment for the semantic interpretation of 3D point cloud data[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-5/W2, 313-318(2013).

    [22] Wang X F, Yang Z X, Cheng X J et al. Efficient registration of forest point clouds by global matching of relative stem positions[EB/OL]. https://arxiv.org/abs/2112.11121

    [23] Cai Z P, Chin T J, Bustos A P et al. Practical optimal registration of terrestrial LiDAR scan pairs[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 118-131(2019).

    [24] Sorkine-Hornung O, Rabinovich M. Least-squares rigid motion using SVD[EB/OL]. https://igl.ethz.ch/projects/ARAP/svd_rot.pdf

    [26] Rusu R B, Cousins S. 3D is here: point cloud library (PCL)[C](2011).

    Zexin Yang, Qin Ye, Xufei Wang, Ravi Peters. Automated Registration of Cross‑Source and Multi‑Temporal Point Clouds in Urban Areas[J]. Chinese Journal of Lasers, 2023, 50(10): 1010004
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