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
- Laser & Optoelectronics Progress
- Vol. 56, Issue 8, 081005 (2019)
Fig. 1. FPS sampling results under different numbers of sampling points. (a) Sparse point sampling; (b) dense point sampling
Fig. 2. Sparse sampling results for 10 sampling points. (a) FPS algorithm in Ref. [20]; (b) proposed algorithm
![Dense sampling results for 1000 sampling points. (a) FPS algorithm in Ref. [20]; (b) proposed algorithm](/Images/icon/loading.gif){kind=icon}
Fig. 3. Dense sampling results for 1000 sampling points. (a) FPS algorithm in Ref. [20]; (b) proposed algorithm
Fig. 4. Comparison of function mapping relationships for horse 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
Fig. 6. Comparison of function mapping relationships for human model cluster. (a) Algorithm in Ref. [21]; (b) proposed algorithm
Fig. 7. Comparison of sparse correspondences for cat 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
Fig. 9. Comparison of dense correspondences for horse 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
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 2. Comparison of execution time for algorithmss
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