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

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 adju...

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Bibliographic Details
Published in:Applied optics (2004) 2021-08, Vol.60 (24), p.7243
Main Authors: Li, Yueyang, Shen, Junfei, Wu, Zhoujie, Zhang, Qican
Format: Article
Language:English
Subjects:
Artificial neural networks
Deep learning
Defocusing
Depth profiling
Error reduction
Phase error
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Summary: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.
ISSN:1559-128X
2155-3165
DOI:10.1364/AO.432085