• Laser & Optoelectronics Progress
  • Vol. 55, Issue 1, 11008 (2018)
Li Renzhong*, Yang Man, Ran Yuan, Zhang Huanhuan, Jing Junfeng, and Li Pengfei
Author Affiliations
  • School of Electronics and Information, Xi''an Polytechnic University, Xi''an, Shaanxi, 710048, China
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    DOI: 10.3788/LOP55.011008 Cite this Article Set citation alerts
    Li Renzhong, Yang Man, Ran Yuan, Zhang Huanhuan, Jing Junfeng, Li Pengfei. Point Cloud Denoising and Simplification Algorithm Based on Method Library[J]. Laser & Optoelectronics Progress, 2018, 55(1): 11008 Copy Citation Text show less
    Flow chart of the proposed algorithm
    Fig. 1. Flow chart of the proposed algorithm
    Principle sketch map of the statistical filter
    Fig. 2. Principle sketch map of the statistical filter
    Principle sketch map of the radius filter
    Fig. 3. Principle sketch map of the radius filter
    Schematic of k-neighbors voxel grid simplification[13]
    Fig. 4. Schematic of k-neighbors voxel grid simplification[13]
    Schematic of triangular mesh reconstruction[13]
    Fig. 5. Schematic of triangular mesh reconstruction[13]
    Test results of 45° human point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction
    Fig. 6. Test results of 45° human point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction
    Test results of 180° human point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction
    Fig. 7. Test results of 180° human point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction
    Test results of self point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction
    Fig. 8. Test results of self point cloud model using the proposed algorithm. (a) Original point cloud; (b) large scale denoising; (c) small scale denoising; (d) uniform simplification; (e) reconstruction
    IndexPoint cloudOriginalpoint cloudLarge scaledenoisingSmall scaledenoisingUniformsimplificationReconstruction
    Point number45° human27911252782605439773977
    180° human30424299102896053145314
    Self12902612458212391161686168
    Running time /ms45° human-613+72085629921856
    180° human-523+908123927952620
    Self-1621791614952783425
    Table 1. Running time of each program module and variations of the point number of the point cloud
    Li Renzhong, Yang Man, Ran Yuan, Zhang Huanhuan, Jing Junfeng, Li Pengfei. Point Cloud Denoising and Simplification Algorithm Based on Method Library[J]. Laser & Optoelectronics Progress, 2018, 55(1): 11008
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