[1] K Harding. Industrial metrology: engineering precision. Nature Photonics, 2, 667(2008).

[2] T Luhmann. Close range photogrammetry for industrial applications. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 558-569(2010).

[3] Ma Y, Soatto S, Koseck J, et al. An Invitation to 3D Vision: from Images to Geometric Models[M]. New Yk: Springer Science & Business Media, 2012, 26.

[4] H Jiang, H Zhao, X Li. High dynamic range fringe acquisition: A novel 3-D scanning technique for high-reflective surfaces. Optics and Lasers in Engineering, 50, 1484-1493(2012).

[5] J Salvi, S Fernandez, T Pribanic. A state of the art in structured light patterns for surface profilometry. Pattern Recognition, 43, 2666-2680(2010).

[6] S Feng, C Zuo, T Tao. Robust dynamic 3-D measurements with motion-compensated phase-shifting profilometry. Optics and Lasers in Engineering, 103, 127-138(2018).

[7] Z H Zhang. Review of single-shot 3D shape measurement by phase calculation-based fringe projection techniques. Optics and Lasers in Engineering, 50, 1097-1106(2012).

[8] X Su, Q Zhang. Dynamic 3-D shape measurement method: a review. Optics and Lasers in Engineering, 48, 191-204(2010).

[9] Y Wang, Z Liu, C Jiang. Motion induced phase error reduction using a Hilbert transform. Optics Express, 26, 34224(2018).

[10] S Feng, Q Chen, C Zuo. Fast three-dimensional measurements for dynamic scenes with shiny surfaces. Optics Communications, 382, 18-27(2017).

[11] S Heist, P Lutzke, I Schmidt. High-speed three-dimensional shape measurement using GOBO projection. Optics and Lasers in Engineering, 87, 90-96(2016).

[12] S Borowiec. AlphaGo seals 4-1 victory over Go grandmaster Lee Sedol. The Guardian, 15(2016).

[13] ZˇBONTAR J, Lecun Y. Stereo matching by training a convolutional neural wk to compare image patches[J]. The Journal of Machine Learning Research, 2016, 17(1): 22872318.

[14] Luo W, Schwing A G, Urtasun R. Efficient deep learning f stereo matching[C]IEEE Conference on Computer Vision Pattern Recognition (CVPR), 2016: 56955703.

[15] Li S, Deng M, Lee J, et al. Imaging through glass diffusers using densely connected convolutional wks[J]. arXiv: 1711.06810[physics], 2017.

[16] Miya T, Roth H R, Nakamura S, et al. Unsupervised segmentation of 3D medical images based on clustering deep representation learning[J]. arXiv: 1804.03830[cs], 2018: 71.

[17] Li H, Wei T, Ren A, et al. Deep reinfcement learning: framewk, applications, embedded implementations[J]. arXiv: 1710.03792[cs], 2017.

[18] Kuznietsov Y, Stuckler J, Leibe B. Semisupervised deep learning f monocular depth map prediction[C]2017 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Honolulu, HI: IEEE, 2017: 22152223.

[19] Kendall A, Grimes M, Cipolla R. Pose: A convolutional wk f realtime 6DOF camera relocalization[C]2015 IEEE International Conference on Computer Vision (ICCV), 2015: 29382946.

[20] H Wang, Y Rivenson, Y Jin. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nature Methods, 16, 103-110(2019).

[21] Y Rivenson, Y Zhang, H GÜNAYDIN. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light: Science & Applications, 7, 17141(2018).

[22] T Nguyen, Y Xue, Y Li. Deep learning approach for Fourier ptychography microscopy. Optics Express, 26, 26470(2018).

[23] R Horisaki, R Takagi, J Tanida. Learning-based imaging through scattering media. Optics Express, 24, 13738(2016).

[24] M Lyu, W Wang, H Wang. Deep-learning-based ghost imaging. Scientific Reports, 7, 17865(2017).

[25] E Nehme, L E Weiss, T Michaeli. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica, 5, 458-464(2018).

[26] L Fang, D Cunefar, C Wang. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomedical Optics Express, 8, 2732-2744(2017).

[27] Y Li, Y Xue, L Tian. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica, 5, 1181-1190(2018).

[28] S Zhang, P S Huang. Novel method for structured light system calibration. Optical Engineering, 45, 083601(2006).

[29] Y Yin, X Peng, A Li. Calibration of fringe projection profilometry with bundle adjustment strategy. Optics Letters, 37, 542-544(2012).

[30] M Takeda, K Mutoh. Fourier transform profilometry for the automatic measurement of 3-D object shapes. Applied Optics, 22, 3977-3982(1983).

[31] C Zuo, S Feng, L Huang. Phase shifting algorithms for fringe projection profilometry: A review. Optics and Lasers in Engineering, 109, 23-59(2018).

[32] Malacara D. Optical Shop Testing[M]. Hoboken, New Jersey: John Wiley & Sons, 2007, 59.

[33] T Hoang, B Pan, D Nguyen. Generic gamma correction for accuracy enhancement in fringe-projection profilometry. Optics Letters, 35, 1992-1994(2010).

[34] S Feng, L Zhang, C Zuo. High dynamic range 3D measurements with fringe projection profilometry: a review. Measurement Science and Technology, 29, 122001(2018).

[35] X Su, W Chen. Reliability-guided phase unwrapping algorithm: a review. Optics and Lasers in Engineering, 42, 245-261(2004).

[36] C Zuo, L Huang, M Zhang. Temporal phase unwrapping algorithms for fringe projection profilometry: A comparative review. Optics and Lasers in Engineering, 85, 84-103(2016).

[37] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision[M]. Cambridge: Cambridge University Press, 2004: 673.

[38] Q Kemao. Two-dimensional windowed Fourier transform for fringe pattern analysis: principles, applications and implementations. Optics and Lasers in Engineering, 45, 304-317(2007).

[39] J Zhong, J Weng. Spatial carrier-fringe pattern analysis by means of wavelet transform: wavelet transform profilometry. Applied Optics, 43, 4993-4998(2004).

[40] S Feng, Q Chen, G Gu. Fringe pattern analysis using deep learning. Advanced Photonics, 1, 1(2019).

[41] J Shi, X Zhu, H Wang. Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement. Optics Express, 27, 28929(2019).

[42] K Yan, Y Yu, C Hu. Fringe pattern denoising based on deep learning. Optics Communications, 437, 148-152(2019).

[43] G E Spoorthi, S Gorthi, R K S S Gorthi. PhaseNet: A deep convolutional neural network for two-dimensional phase unwrapping. IEEE Signal Processing Letters, 26, 54-58(2019).

[44] K Wang, Y Li, Q Kemao. One-step robust deep learning phase unwrapping. Optics Express, 27, 15100(2019).

[45] W Yin, Q Chen, S Feng. Temporal phase unwrapping using deep learning. Scientific Reports, 9, 20175(2019).

[46] Der Jeught S Van, J J J Dirckx. Deep neural networks for single shot structured light profilometry. Optics Express, 27, 17091(2019).

[47] S Lv, Q Sun, Y Zhang. Projector distortion correction in 3D shape measurement using a structured-light system by deep neural networks. Optics Letters, 45, 204-207(2020).

[48] C Zuo, T Tao, S Feng. Micro Fourier Transform Profilometry (μ FTP): 3D shape measurement at 10,000 frames per second. Optics and Lasers in Engineering, 102, 70-91(2018).

[49] S Feng, C Zuo, W Yin. Micro deep learning profilometry for high-speed 3D surface imaging. Optics and Lasers in Engineering, 121, 416-427(2019).

[50] Zeiler M D, Fergus R. Visualizing understing convolutional wks[J]. arXiv: 1311.2901[cs], 2013.

[51] J Bergstra, Y Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281-305(2012).

[52] Finn C, Abbeel P, Levine S. Modelagnostic metalearning f fast adaptation of deep wks[C]Proceedings of the 34th International Conference on Machine LearningVolume 70. JMLR. g, 2017: 1126−1135.

[53] Zoph B, Le Q V. Neural architecture search with reinfcement learning[J]. arXiv preprint arXiv: 1611.01578, 2016.

[54] Tan C, Sun F, Kong T, et al. A survey on deep transfer learning[C]International Conference on Artificial Neural wks, 2018: 270−279.