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Laser & Optoelectronics Progress, Volume. 56, Issue 8, 081005(2019)

Correspondence Calculation of Model Cluster by Functional Mapping Combined with Cycle-Consistency Constraints

Jun Yang* and Ming Lei
Author Affiliations
  • School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (21)
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    Figures & Tables(13)
    FPS sampling results under different numbers of sampling points. (a) Sparse point sampling; (b) dense point sampling

    Fig. 1. FPS sampling results under different numbers of sampling points. (a) Sparse point sampling; (b) dense point sampling

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    Sparse sampling results for 10 sampling points. (a) FPS algorithm in Ref. [20]; (b) proposed algorithm

    Fig. 2. Sparse sampling results for 10 sampling points. (a) FPS algorithm in Ref. [20]; (b) proposed algorithm

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    Dense sampling results for 1000 sampling points. (a) FPS algorithm in Ref. [20]; (b) proposed algorithm

    Fig. 3. Dense sampling results for 1000 sampling points. (a) FPS algorithm in Ref. [20]; (b) proposed algorithm

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    Comparison of function mapping relationships for horse model cluster. (a) Algorithm in Ref. [21]; (b) proposed algorithm

    Fig. 4. Comparison of function mapping relationships for horse model cluster. (a) Algorithm in Ref. [21]; (b) proposed algorithm

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    Comparison of function mapping relationships for dog model cluster. (a) Algorithm in Ref. [21]; (b) proposed algorithm

    Fig. 5. Comparison of function mapping relationships for dog model cluster. (a) Algorithm in Ref. [21]; (b) proposed algorithm

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    Comparison of function mapping relationships for human model cluster. (a) Algorithm in Ref. [21]; (b) proposed algorithm

    Fig. 6. Comparison of function mapping relationships for human model cluster. (a) Algorithm in Ref. [21]; (b) proposed algorithm

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    Comparison of sparse correspondences for cat model cluster. (a) Algorithm in Ref. [20]; (b) proposed algorithm

    Fig. 7. Comparison of sparse correspondences for cat model cluster. (a) Algorithm in Ref. [20]; (b) proposed algorithm

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    Comparison of sparse correspondences for dog model cluster. (a) Algorithm in Ref. [20]; (b) proposed algorithm

    Fig. 8. Comparison of sparse correspondences for dog model cluster. (a) Algorithm in Ref. [20]; (b) proposed algorithm

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    Comparison of dense correspondences for horse model cluster. (a) Algorithm in Ref. [20]; (b) proposed algorithm

    Fig. 9. Comparison of dense correspondences for horse model cluster. (a) Algorithm in Ref. [20]; (b) proposed algorithm

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    Comparison of dense correspondences for human model cluster. (a) Algorithm in Ref.[20]; (b) proposed algorithm

    Fig. 10. Comparison of dense correspondences for human model cluster. (a) Algorithm in Ref.[20]; (b) proposed algorithm

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    Comparison of correspondences for incomplete model clusters. (a) Cat model; (b) dog model; (c) horse model

    Fig. 11. Comparison of correspondences for incomplete model clusters. (a) Cat model; (b) dog model; (c) horse model

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    • Table 1. Comparison of geodesic errors for correspondence between models

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      Table 1. Comparison of geodesic errors for correspondence between models

      Types of three-dimensional modelSparse correspondence (100 vertices)Dense correspondence (1000 vertices)
      Proposed algorithmAlgorithm of Ref. [20]Proposed algorithmAlgorithm of Ref. [20]
      Horse0.0150.0260.0240.426
      Human (FAUST)0.0160.0280.0150.028
      Dog0.0200.0240.0270.306
      Human (TOSCA)0.0110.0150.0400.386
      Cat0.0160.0180.0600.482
      Gorilla0.0130.0160.0150.203
      Human (SCAPE)0.0090.0150.0900.101

    • Table 2. Comparison of execution time for algorithmss

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      Table 2. Comparison of execution time for algorithmss

      Type of three-dimensional modelSparse correspondence (100 vertices)Dense correspondence (1000 vertices)
      Proposed algorithmAlgorithmin Ref. [20]Proposed algorithmAlgorithmin Ref. [20]
      Horse36.15637.23243.60346.130
      Human (FAUST)41.54742.47558.98059.128
      Dog24.34124.45347.35948.128
      Human (TOSCA)38.46838.50349.34551.342
      Cat26.68028.89544.39646.987
      Gorilla21.39522.67039.58741.245
      Human (SCAPE)32.65434.92358.43261.361
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    Jun Yang, Ming Lei. Correspondence Calculation of Model Cluster by Functional Mapping Combined with Cycle-Consistency Constraints[J]. Laser & Optoelectronics Progress, 2019, 56(8): 081005

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    Paper Information

    Category: Image Processing

    Received: Oct. 17, 2018

    Accepted: Nov. 13, 2018

    Published Online: Jul. 26, 2019

    The Author Email: Jun Yang (yangj@mail.lzjtu.cn)

    DOI:10.3788/LOP56.081005

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology