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Passive binary defocusing for large depth 3D measurement based on deep learning

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Abstract

Phase-shifting profilometry (PSP) based on the binary defocusing technique has been widely used due to its high-speed capability. However, the required adjustment in projector defocus by traditional method is inaccurate, inflexible, and associated with fringe pitch. Instead of manual defocusing adjustment, a passive defocus of the binary patterns based on deep learning is proposed in this paper. Learning the corresponding binary patterns with a specifically designed convolutional neural network, high-quality three-step sinusoidal patterns can be generated. Experimental results demonstrate that the proposed method could reduce phase error by 80%–90% for different fringe pitches without projector defocus and outperform the traditional method by providing more accurate and robust results within a large measuring depth.

© 2021 Optical Society of America

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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