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Laser & Optoelectronics Progress, Volume. 59, Issue 16, 1610001(2022)

Depth Estimation from Single-Frame Fringe Projection Patterns Based on R2U-Net

Mengkai Yuan1, Xinjun Zhu2、*, and Linpeng Hou1
Author Affiliations
  • 1School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
  • 2School of Artificial Intelligence, Tiangong University, Tianjin 300387, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (17)
    Equations (6)
    References (29)
    Cited By (4)
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    Figures & Tables(17)
    Recurrent residual convolutional neural network based on U-Net

    Fig. 1. Recurrent residual convolutional neural network based on U-Net

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    Recurrent residual convolutional units and unfolded recurrent convolutional units

    Fig. 2. Recurrent residual convolutional units and unfolded recurrent convolutional units

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    Schematic diagram of proposed algorithm

    Fig. 3. Schematic diagram of proposed algorithm

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    Simulated projection fringe pattern and simulated depth map. (a) Simulated projection fringe pattern; (b) simulated depth map

    Fig. 4. Simulated projection fringe pattern and simulated depth map. (a) Simulated projection fringe pattern; (b) simulated depth map

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    Simulated training dataset

    Fig. 5. Simulated training dataset

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    Error of R2U-Net and U-Net under free noise testing samples

    Fig. 6. Error of R2U-Net and U-Net under free noise testing samples

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    Depth map prediction result of simulated data. (a) Simulated fringe pattern of test input; (b) depth map corresponding to fringe pattern; (c) prediction result of U-Net; (d) prediction result of R2U-Net; (e) comparison of 270th row of prediction result

    Fig. 7. Depth map prediction result of simulated data. (a) Simulated fringe pattern of test input; (b) depth map corresponding to fringe pattern; (c) prediction result of U-Net; (d) prediction result of R2U-Net; (e) comparison of 270th row of prediction result

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    Comparison of R2U-Net method and FTM method. (a) Depth map corresponding to fringe pattern; (b) prediction result of R2U-Net; (c) result of FTM; (d) comparison of the 270th row of the prediction result

    Fig. 8. Comparison of R2U-Net method and FTM method. (a) Depth map corresponding to fringe pattern; (b) prediction result of R2U-Net; (c) result of FTM; (d) comparison of the 270th row of the prediction result

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    Error of R2U-Net and U-Net under noisy testing samples

    Fig. 9. Error of R2U-Net and U-Net under noisy testing samples

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    Depth map prediction result of noise simulated data. (a) Simulated fringe pattern of test input; (b) depth map corresponding to fringe pattern; (c) prediction result of U-Net; (d) prediction result of R2U-Net; (e) comparison of 270th row of prediction result

    Fig. 10. Depth map prediction result of noise simulated data. (a) Simulated fringe pattern of test input; (b) depth map corresponding to fringe pattern; (c) prediction result of U-Net; (d) prediction result of R2U-Net; (e) comparison of 270th row of prediction result

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    Comparison of R2U-Net method and FTM method (Noise simulation data). (a) Depth map corresponding to fringe pattern; (b) prediction result of R2U-Net; (c) result of FTM; (d) comparison of 270th row of prediction result

    Fig. 11. Comparison of R2U-Net method and FTM method (Noise simulation data). (a) Depth map corresponding to fringe pattern; (b) prediction result of R2U-Net; (c) result of FTM; (d) comparison of 270th row of prediction result

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    Experimental training dataset

    Fig. 12. Experimental training dataset

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    Error of R2U-Net and U-Net under experimental testing samples

    Fig. 13. Error of R2U-Net and U-Net under experimental testing samples

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    Depth map prediction result of experimental sample. (a) Experimental fringe pattern of test input; (b) depth map corresponding to fringe pattern; (c) prediction result of U-Net; (d) prediction result of R2U-Net; (e) comparison of 310th row of prediction result

    Fig. 14. Depth map prediction result of experimental sample. (a) Experimental fringe pattern of test input; (b) depth map corresponding to fringe pattern; (c) prediction result of U-Net; (d) prediction result of R2U-Net; (e) comparison of 310th row of prediction result

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    Depth map prediction result of the second experimental sample. (a) Simulated fringe pattern of the test input; (b) depth map corresponding to fringe pattern; (c) prediction result of U-Net; (d) prediction result of R2U-Net; (e) comparison of 320th row of prediction result

    Fig. 15. Depth map prediction result of the second experimental sample. (a) Simulated fringe pattern of the test input; (b) depth map corresponding to fringe pattern; (c) prediction result of U-Net; (d) prediction result of R2U-Net; (e) comparison of 320th row of prediction result

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    • Table 1. Comparison of three loss functions

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      Table 1. Comparison of three loss functions

      Loss functionMSE
      MSE1.71×10-6
      SSIM2.22×10-6
      SSIM-MAE2.17×10-6

    • Table 2. Performance evaluation of the two models

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      Table 2. Performance evaluation of the two models

      ModelMAESSIMMSE
      U-Net8.62×10-30.984951.24×10-3
      R2U-Net7.12×10-30.987751.08×10-3
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    Mengkai Yuan, Xinjun Zhu, Linpeng Hou. Depth Estimation from Single-Frame Fringe Projection Patterns Based on R2U-Net[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1610001

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    Paper Information

    Category: Image Processing

    Received: May. 10, 2021

    Accepted: Jun. 27, 2021

    Published Online: Jul. 22, 2022

    The Author Email: Xinjun Zhu (xinjunzhu@tiangong.edu.cn)

    DOI:10.3788/LOP202259.1610001

    Topics

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