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Chinese Journal of Lasers, Volume. 47, Issue 12, 1204005(2020)

Three-Dimensional Measurement Method of Light Field Imaging Based on Deep Learning

Wu Junlong1,2,3, Guo Zhenghua1,2,3, Chen Xianfeng1,2,3, Ma Shuai1,2,3, Yan Xu1,2,3, Zhu Licheng1,2,3, Wang Shuai1,3, and Yang Ping1,3、*
Author Affiliations
  • 1Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
  • 2Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (25)
    Cited By (12)
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    Figures & Tables(13)
    Projection model of light field camera

    Fig. 1. Projection model of light field camera

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    Relationship between disparity and depth

    Fig. 2. Relationship between disparity and depth

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    Illustration of equivalent baselines

    Fig. 3. Illustration of equivalent baselines

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    Structure of network

    Fig. 4. Structure of network

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    Illustration of network input

    Fig. 5. Illustration of network input

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    Center view and GT disparity of synthetic datasets

    Fig. 6. Center view and GT disparity of synthetic datasets

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    Test results of synthetic dataset

    Fig. 7. Test results of synthetic dataset

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    Test results of real dataset

    Fig. 8. Test results of real dataset

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    Three-dimensional reconstruction results of test scene

    Fig. 9. Three-dimensional reconstruction results of test scene

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    Measurement of real scene scale. (a) Test scene; (b) disparity map; (c) three-dimensional reconstruction structure

    Fig. 10. Measurement of real scene scale. (a) Test scene; (b) disparity map; (c) three-dimensional reconstruction structure

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    • Table 1. Badpixel(0.07) comparison of different algorithms

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      Table 1. Badpixel(0.07) comparison of different algorithms

      SceneLF_OCCEPI2LFFocalStackNetEPInetProposed
      Boxes26.5229.8023.0214.3312.2411.46
      Cotton6.2216.697.830.580.460.51
      Dino14.9115.6719.032.531.261.14
      Sideboard18.5018.9521.995.404.784.56
      Backgammon19.0122.085.524.343.281.87
      Pyramids3.171.0812.350.290.150.28
      Dots5.8246.532.901.021.983.49
      Stripes18.4123.8135.743.720.910.85

    • Table 2. MSE comparison of different algorithms

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      Table 2. MSE comparison of different algorithms

      SceneLF_OCCEPI2LFFocalStackNetEPInetProposed
      Boxes9.8510.9317.4311.826.014.81
      Cotton1.074.329.170.880.220.23
      Dino1.142.071.160.890.150.15
      Sideboard2.304.655.071.960.810.59
      Backgammon21.5920.7813.016.583.912.39
      Pyramids0.100.020.270.020.0070.01
      Dots3.306.665.681.871.984.29
      Stripes8.136.1017.451.790.910.85

    • Table 3. Runtime comparison of different algorithms unit: s

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      Table 3. Runtime comparison of different algorithms unit: s

      SceneLF_OCCEPI2LFFocalStackNetEPInetProposed
      Boxes10408.268.91962.1885.042.031.03
      Cotton6325.519.07984.5384.902.031.04
      Dino10099.058.091130.5685.622.031.07
      Sideboard13531.308.74987.4784.772.021.11
      Backgammon5116.256.93979.8784.242.021.04
      Pyramids11688.426.88929.7292.092.021.04
      Dots10820.857.66979.8781.562.031.08
      Stripes19331.428.531093.9891.922.031.07
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    Wu Junlong, Guo Zhenghua, Chen Xianfeng, Ma Shuai, Yan Xu, Zhu Licheng, Wang Shuai, Yang Ping. Three-Dimensional Measurement Method of Light Field Imaging Based on Deep Learning[J]. Chinese Journal of Lasers, 2020, 47(12): 1204005

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    Paper Information

    Category: Measurement and metrology

    Received: Jun. 9, 2020

    Accepted: --

    Published Online: Nov. 18, 2020

    The Author Email: Ping Yang (pingyang2516@163.com)

    DOI:10.3788/CJL202047.1204005

    Topics

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