• Acta Optica Sinica
  • Vol. 40, Issue 16, 1610002 (2020)
Wen Yang, Mingquan Zhou*, Bao Guo, Guohua Geng, Xiaoning Liu, and Yangyang Liu
Author Affiliations
  • College of Information Science and Technology, Northwest University, Xi′an, Shaanxi 710127, China
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    DOI: 10.3788/AOS202040.1610002 Cite this Article Set citation alerts
    Wen Yang, Mingquan Zhou, Bao Guo, Guohua Geng, Xiaoning Liu, Yangyang Liu. Skull Point Cloud Registration Method Based on Curvature Maps[J]. Acta Optica Sinica, 2020, 40(16): 1610002 Copy Citation Text show less
    Diagram of local coordinate system and point projection. (a) Feature point p and its neighborhood point {pi} in local coordinate system op-xpypzp; (b) projection points on two-dimensional plane
    Fig. 1. Diagram of local coordinate system and point projection. (a) Feature point p and its neighborhood point {pi} in local coordinate system op-xpypzp; (b) projection points on two-dimensional plane
    Sub-regions of regional curvature map
    Fig. 2. Sub-regions of regional curvature map
    Flow chart of proposed algorithm
    Fig. 3. Flow chart of proposed algorithm
    Iterative convergence curve
    Fig. 4. Iterative convergence curve
    Skull point cloud models to be registered
    Fig. 5. Skull point cloud models to be registered
    Registration results of different rough registration methods. (a) PCA; (b) GA; (c) FPFH; (d) proposed method
    Fig. 6. Registration results of different rough registration methods. (a) PCA; (b) GA; (c) FPFH; (d) proposed method
    Two skulls to be registered
    Fig. 7. Two skulls to be registered
    Coarse registration results
    Fig. 8. Coarse registration results
    Fine registration results of ICP algorithm
    Fig. 9. Fine registration results of ICP algorithm
    Fine registration results of improved ICP algorithm
    Fig. 10. Fine registration results of improved ICP algorithm
    Initial position
    Fig. 11. Initial position
    Coarse registration result
    Fig. 12. Coarse registration result
    Fine registration results of ICP algorithm
    Fig. 13. Fine registration results of ICP algorithm
    Fine registration results of improved ICP algorithm
    Fig. 14. Fine registration results of improved ICP algorithm
    MethodRegistration error /mmTime-consuming /s
    PCA6.254×10-120.39
    GA5.697×10-124.96
    FPFH4.638×10-116.84
    Proposed method3.474×10-118.25
    Table 1. Comparison of registration efficiency of different rough registration methods
    AlgorithmNumber of iterationsRegistration error /mmTime-consuming /s
    ICP463.652×10-247.83
    Improved ICP293.243×10-231.64
    Table 2. Comparison of registration efficiency of skull point clouds
    AlgorithmNumber of iterationsRegistration error /mmTime-consuming /s
    ICP328.495×10-321.63
    Improved ICP187.876×10-312.28
    Table 3. Comparison of registration efficiency of bunny point clouds
    Point cloud modelNumber of point cloudsAlgorithmNumber of iterationsRegistration error /mmTime-consuming /s
    LO-RANSAC[26]485.689×10-247.86
    Skull to be registered210759Super-4PCS[27]354.384×10-232.75
    Go-ICP[28]534.930×10-252.59
    PICP[29]423.677×10-241.06
    Reference skull211234IRLS-ICP[30]373.256×10-236.63
    Proposed algorithm302.977×10-229.67
    LO-RANSAC[26]292.899×10-218.21
    Bunny to be registered40000Super-4PCS[27]191.556×10-213.22
    Go-ICP[28]312.017×10-220.69
    PICP[29]251.154×10-216.95
    Reference bunny40000IRLS-ICP[30]229.255×10-315.48
    Proposed algorithm177.688×10-311.94
    Table 4. Efficiency comparison of different registration algorithms
    Wen Yang, Mingquan Zhou, Bao Guo, Guohua Geng, Xiaoning Liu, Yangyang Liu. Skull Point Cloud Registration Method Based on Curvature Maps[J]. Acta Optica Sinica, 2020, 40(16): 1610002
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