• Infrared and Laser Engineering
  • Vol. 51, Issue 6, 20210949 (2022)
Shuaitai Zhang1、2, Guoyuan Li2、*, Xiaoqing Zhou2, Jiaqi Yao2、3, Jinquan Guo2, and Xinming Tang1、2
Author Affiliations
  • 1College of Mapping and Geographics, Lanzhou Jiaotong University, Lanzhou 730070, China
  • 2Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of P. R. China, Beijing 100048, China
  • 3College of Geodesy Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
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    DOI: 10.3788/IRLA20210949 Cite this Article
    Shuaitai Zhang, Guoyuan Li, Xiaoqing Zhou, Jiaqi Yao, Jinquan Guo, Xinming Tang. Single photon point cloud denoising algorithm based on multi-features adaptive[J]. Infrared and Laser Engineering, 2022, 51(6): 20210949 Copy Citation Text show less
    Flow chart of algorithm
    Fig. 1. Flow chart of algorithm
    Number of signal pulses in different filter core shapes
    Fig. 2. Number of signal pulses in different filter core shapes
    Slope adaptation in flat areas
    Fig. 3. Slope adaptation in flat areas
    Slope adaptation in areas with large gradient
    Fig. 4. Slope adaptation in areas with large gradient
    Histogram of point cloud density
    Fig. 5. Histogram of point cloud density
    Point cloud density histogram with different noise rates
    Fig. 6. Point cloud density histogram with different noise rates
    Data distribution in the study area
    Fig. 7. Data distribution in the study area
    Original point cloud of data A
    Fig. 8. Original point cloud of data A
    Original point cloud of data B
    Fig. 9. Original point cloud of data B
    Adaptive results of spatial density of data A
    Fig. 10. Adaptive results of spatial density of data A
    Adaptive results of spatial density of data B
    Fig. 11. Adaptive results of spatial density of data B
    Final denoising result of data A
    Fig. 12. Final denoising result of data A
    Final denoising result of data B
    Fig. 13. Final denoising result of data B
    Comparison between signal point cloud extracted in this paper and ATL03 signal in data A
    Fig. 14. Comparison between signal point cloud extracted in this paper and ATL03 signal in data A
    Comparison between signal point cloud extracted in this paper and ATL03 signal in data B
    Fig. 15. Comparison between signal point cloud extracted in this paper and ATL03 signal in data B
    Comparison between signal point cloud extracted in this paper and ATL08 signal in data A
    Fig. 16. Comparison between signal point cloud extracted in this paper and ATL08 signal in data A
    Comparison between signal point cloud extracted in this paper and ATL08 signal in data B
    Fig. 17. Comparison between signal point cloud extracted in this paper and ATL08 signal in data B
    Partial visual analysis of data A
    Fig. 18. Partial visual analysis of data A
    Partial visual analysis of data B
    Fig. 19. Partial visual analysis of data B
    Results of data A based on circular filter kernel
    Fig. 20. Results of data A based on circular filter kernel
    Result of data A based on elliptic filter kernel
    Fig. 21. Result of data A based on elliptic filter kernel
    ItemData AData B
    R20.990.99
    RMSE/MHz2.9×10−21.15×10−2
    Maximum noise rate/MHz8.565.87
    Table 1. Comparison between ATL03 results and noise rate results proposed in the paper
    ItemData AData B
    ATL08Proposed algorithmATL08Proposed algorithm
    Number of signal intersection points9076251128
    Total signal points908321262855133269602
    Proportion of intersection number0.990.720.990.73
    Proportion of intersection distance0.990.820.990.83
    Table 2. Comparison between ATL08 results and the results in this paper
    Shuaitai Zhang, Guoyuan Li, Xiaoqing Zhou, Jiaqi Yao, Jinquan Guo, Xinming Tang. Single photon point cloud denoising algorithm based on multi-features adaptive[J]. Infrared and Laser Engineering, 2022, 51(6): 20210949
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