• Laser & Optoelectronics Progress
  • Vol. 59, Issue 18, 1828005 (2022)
Yongyu Wei, Chunkang Zhang*, Xiaomei Shao, Yutian Ji, and Yao Yin
Author Affiliations
  • College of Mining, Guizhou University, Guiyang 550025, Guizhou, China
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    DOI: 10.3788/LOP202259.1828005 Cite this Article Set citation alerts
    Yongyu Wei, Chunkang Zhang, Xiaomei Shao, Yutian Ji, Yao Yin. Extraction and Simplification of three-dimensional Point Cloud Topological Features Using Piecewise Linear Morse Theory[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1828005 Copy Citation Text show less
    Computation of index function
    Fig. 1. Computation of index function
    Weight computation of feature points
    Fig. 2. Weight computation of feature points
    Homomorphic shrinkage algorithm. (a) Before simplification; (b) after simplification
    Fig. 3. Homomorphic shrinkage algorithm. (a) Before simplification; (b) after simplification
    Schematic of retention index
    Fig. 4. Schematic of retention index
    Flow chart of the proposed algorithm
    Fig. 5. Flow chart of the proposed algorithm
    Raw data. (a) kerolamp model; (b) helix model
    Fig. 6. Raw data. (a) kerolamp model; (b) helix model
    Delaunay triangulation construction. (a) kerolamp model; (b) helix model
    Fig. 7. Delaunay triangulation construction. (a) kerolamp model; (b) helix model
    Piecewise linear falling Morse complex extracted by the proposed algorithm. (a) kerolamp model; (b) helix model
    Fig. 8. Piecewise linear falling Morse complex extracted by the proposed algorithm. (a) kerolamp model; (b) helix model
    Piecewise linear falling Morse complex extracted by the method in Ref.[24]. (a) kerolamp model; (b) helix model
    Fig. 9. Piecewise linear falling Morse complex extracted by the method in Ref.[24]. (a) kerolamp model; (b) helix model
    Feature extraction results for kerolamp model in different thresholds. (a) λ=0.8,XT =0.23; (b) λ=0.8,XT =0.3; (c) λ=0.8,XT =0.32
    Fig. 10. Feature extraction results for kerolamp model in different thresholds. (a) λ=0.8,XT =0.23; (b) λ=0.8,XT =0.3; (c) λ=0.8,XT =0.32
    Feature extraction results for helix model in different thresholds. (a) λ=0.7,XT=0.15; (b) λ=0.7,XT=0.2; (c) λ=0.7,XT=0.22
    Fig. 11. Feature extraction results for helix model in different thresholds. (a) λ=0.7,XT=0.15; (b) λ=0.7,XT=0.2; (c) λ=0.7,XT=0.22
    Feature extraction results for model 1 in different noise levels. (a) 0 dB; (b) 10 dB; (c) 20 dB; (d) 50 dB
    Fig. 12. Feature extraction results for model 1 in different noise levels. (a) 0 dB; (b) 10 dB; (c) 20 dB; (d) 50 dB
    MethodNumber of extracted featuresRuning time /sPoint cloud compression ratio /%
    Proposed method10680.828.75
    Method in Ref.[2414052.76.27
    Table 1. Comparison of different methods for kerolamp model
    Yongyu Wei, Chunkang Zhang, Xiaomei Shao, Yutian Ji, Yao Yin. Extraction and Simplification of three-dimensional Point Cloud Topological Features Using Piecewise Linear Morse Theory[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1828005
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