Deep learning-based fringe pattern transformation method for phase calculation

Haotian Yu; Yang Zhao; Dongliang Zheng; Jing Han; Yi Zhang

doi:10.1117/12.2586621

15 January 2021 Deep learning-based fringe pattern transformation method for phase calculation

Haotian Yu, Yang Zhao, Dongliang Zheng, Jing Han, Yi Zhang

Proceedings Volume 11761, Fourth International Conference on Photonics and Optical Engineering; 1176118 (2021) https://doi.org/10.1117/12.2586621
Event: Fourth International Conference on Photonics and Optical Engineering, 2020, Xi'an, China

Abstract

Fringe projection profilometry (i.e., FPP) has been one of the most popular techniques in three-dimensional (i.e., 3-D) measurement. In FPP, it is necessary to obtain accurate desired phase by using a small number of fringes in dynamic measurement. Recently, fringe pattern transformation method (i.e., FPTM) is proposed based on deep learning, which can achieve accurate 3-D measurement using a single fringe, but the phase error is still higher than the phase-shifting algorithm. In this paper, the phase error of FPTM is analyzed and the relationship between it and local depth change rate is illustrated firstly. Then, the accuracy of FPTM can be improved by using more fringes. Compared with traditional methods, FPTM can achieve higher precision 3-D measurement when less fringes are used.

Citation Download Citation

Haotian Yu, Yang Zhao, Dongliang Zheng, Jing Han, and Yi Zhang "Deep learning-based fringe pattern transformation method for phase calculation", Proc. SPIE 11761, Fourth International Conference on Photonics and Optical Engineering, 1176118 (15 January 2021); https://doi.org/10.1117/12.2586621

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