Infrared and Laser Engineering, Volume. 49, Issue 6, 20200023(2020)

Multi-stage deep learning based single-frame fringe projection 3D measurement method

Zhao Zhang... Bowen Han, Haotian Yu, Yi Zhang, Dongliang Zheng and Jing Han* |Show fewer author(s)
Author Affiliations
  • 南京理工大学 电子工程与光电技术学院,江苏 南京 210094
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    Figures & Tables(10)
    3D measurement of traditional digital fringe projection. (a) Fringe projection; (b) wrapped phase; (c) absolute phase; (d) depth
    Single-stage deep learning based single-frame fringe projection 3D measurement method. (a)Fringe projection; (b)UNet; (c)Depth
    Multi-stage deep learning based single-frame fringe projection 3D measurement method. (a) Fringe; (b) FPTNet; (c) s phase-shifted fringe with different frequencies; (d) absolute phase; (e) PDNet; (f) depth
    3D measurement results of two methods for a simple morphologic object. (a) Fringe projection; (b) measurement result of Deeplab V3+; (c) measurement result of ERFNet; (d) measurement result of UNet; (e) measurement result of the proposed method; (f) ground truth; (g) measurement error of DeeplabV3+; (h) measurement error of ERFNet; (i) measurement error of UNet; (j) measurement error of the proposed method
    3D measurement results of two methods for a complex morphologic object. (a) Fringe projection; (b) measurement result of Deeplab V3+; (c) measurement result of ERFNet; (d) measurement result of UNet; (e) measurement result of the proposed method; (f) ground truth; (g) measurement error of Deeplab V3+; (h) measurement error of ERFNet; (i) measurement error of UNet; (j) measurement error of the proposed method
    (a) Error of multi-stage deep learning based single-frame fringe projection 3D measurement method; (b) corresponding enlarged detail of the red box in (a); (c) corresponding enlarged detail of the green box in (a)
    • Table 1. Main modules and parameters of PDNet

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      Table 1. Main modules and parameters of PDNet

      LayerTypeOut-FOut-Res
      1Conv3d163×496×496
      2ReLU163×496×496
      3BatchNorm3d163×496×496
      4Conv3d323×496×496
      5ReLU323×496×496
      6BatchNorm3d323×496×496
      7Conv3d643×496×496
      8ReLU643×496×496
      9BatchNorm3d643×496×496
      10Conv3d1283×496×496
      11ReLU1283×496×496
      12BatchNorm3d1283×496×496
      13Conv3d643×496×496
      14ReLU643×496×496
      15BatchNorm3d643×496×496
      16Conv3d323×496×496
      17ReLU323×496×496
      18BatchNorm3d323×496×496
      19Conv3d11×496×496
      20ReLU11×496×496
      21BatchNorm3d11×496×496
    • Table 2. 3D measurement results of two methods

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      Table 2. 3D measurement results of two methods

      MethodNetworkInputParametersRMSE/mm
      Single-stageDeeplab V3+f=64 single-frame fringe 59 350 6739.605
      ERFNet2 063 9229.018
      UNet34 528 7696.911
      Multi-stageFPTNet joint PDNet14 508 7851.408
    • Table 3. Error of multi-stage deep learning based single-frame fringe projection 3D measurement method on C3D test set

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      Table 3. Error of multi-stage deep learning based single-frame fringe projection 3D measurement method on C3D test set

      InputMethodRMSE/mm
      Correct absolute phasePDNet0.493
      Absolute phase obtained by FPTNet1.408
    • Table 4. Accuracy of measuring the standard sphere by using calibration parameter and PDNet

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      Table 4. Accuracy of measuring the standard sphere by using calibration parameter and PDNet

      InputMethodRMSE/mm
      Absolute phaseUsing calibration parameters0.018
      PDNet0.363
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    Zhao Zhang, Bowen Han, Haotian Yu, Yi Zhang, Dongliang Zheng, Jing Han. Multi-stage deep learning based single-frame fringe projection 3D measurement method[J]. Infrared and Laser Engineering, 2020, 49(6): 20200023

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    Paper Information

    Category: Special issue-Optical 3D imaging and sensing

    Received: Mar. 19, 2020

    Accepted: --

    Published Online: Aug. 19, 2020

    The Author Email: Han Jing (eohj@njust.edu.cn)

    DOI:10.3788/IRLA20200023

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