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.
In the recording process of phase-shifting profilometry, intensity fluctuation caused by uorescent light source instability may occur and then introduce a non-ignorable phase error. More importantly, the selection of sampling speed will also affect the value of the phase error, which even up to 0.12 rad. To suppress this problem, a deep learning-based fluorescent light error suppression (DLFLES) method is proposed to achieve high-precise measurement under fluorescent light. Experiments demonstrate that the shapes of the reconstructed 3-D images are more precise using the proposed method. Our research would promote the development of accurate 3-D measurement under the interference of external light sources by using deep learning.
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