Author Affiliations
1Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China2Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, Nanjing 210019, China3Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing 210094, China4Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR 999077, Chinashow less
Fig. 1. Diagrams of the physics-driven method, physics-informed deep learning approach, and data-driven deep learning approach for fringe pattern analysis.
Fig. 2. Overview of the proposed PI-FPA. (a) PI-FPA including a LeFTP module and a lightweight network. (b) Net head and Net tail. (c) The phase retrieval process of the LeFTP module.
Fig. 3. Comparative results for single-shot fringe pattern analysis of the David model. (a–e) The phase retrieval process, wrapped phases, phase errors, and magnified views of the phase errors using FTP, LeFTP, Net head + LeFTP, U-Net, and PI-FPA.
Fig. 4. Comparative fringe analysis results of the industrial part. (a) The industrial part and the phase errors using FTP, U-Net, and PI-FPA. (b) The magnified views of the phase errors. (c) Single-shot 3D imaging results using different methods. (d) The magnified views of (c). (e) The line profiles in (d).
Fig. 5. Precision analysis for a ceramic plane and a standard sphere moving along the Z axis. (a) 3D reconstruction results using PI-FPA at different time points. (b–c) the error distributions of the sphere and plane. (d–e) temporal precision analysis results of the plane and sphere over a 1.62 s period using 3-step PS, FTP, U-Net, and PI-FPA. (f–i) the color-coded 3D reconstruction and the corresponding error distributions of the plane and the standard sphere using different methods at T = 0.81 s.
Fig. 6. Fast 3D measurement results using different fringe pattern analysis methods. (a) The representative fringe images at different time points and the corresponding color-coded 3D reconstructions results for the rotated workpiece model using 3-step PS, FTP, U-Net, and PI-FPA. (b) The representative fringe images at different time points and the corresponding color-coded 3D reconstructions results for non-rigid dynamic face using 3-step PS, FTP, U-Net, and PI-FPA. (c) 360-degree 3D reconstruction of the workpiece model using PI-FPA. (d) 3D measurement results of non-rigid dynamic face using PI-FPA.
Method | Time (ms) | RMS (μm) | Plane | Sphere | 3-step PS | 5.22×10−3 | 188±29.8 | 179±19.9 | FTP | 2.06×10−2 | 77±6.8 | 81±7.4 | U-Net | 65.02 | 56±4.9 | 59±6.6 | PI-FPA | 18.78 | 43±4.1 | 47±5.1 |
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Table 1. Quantitative analysis results of the moving plane and sphere for different methods.