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Fringe projection profilometry (FPP) has become increasingly important indynamic 3-D shape measurement. In FPP, it is necessary to retrieve the phase ofthe measured object before shape profiling. However, traditional phaseretrieval techniques often require a large number of fringes, which maygenerate motion-induced error for dynamic objects. In this paper, a novel phaseretrieval technique based on deep learning is proposed, which uses anend-to-end deep convolution neural network to transform a single or two fringesinto the phase retrieval required fringes. When the object's surface is locatedin a restricted depth, the presented network only requires a single fringe asthe input, which otherwise requires two fringes in an unrestricted depth. Theproposed phase retrieval technique is first theoretically analyzed, and thennumerically and experimentally verified on its applicability for dynamic 3-Dmeasurement.

Dynamic 3-D measurement based on fringe-to-fringe transformation using deep learning