Fast 3D measurement based on fringe projection profilometry and deep learning

Yuxuan Che; Wei Yin

doi:10.1117/12.2642326

21 December 2022 Fast 3D measurement based on fringe projection profilometry and deep learning

Yuxuan Che, Wei Yin

Proceedings Volume 12319, Optical Metrology and Inspection for Industrial Applications IX; 1231915 (2022) https://doi.org/10.1117/12.2642326
Event: SPIE/COS Photonics Asia, 2022, Online Only

Conference Poster

Abstract

Deep learning is currently gaining a lot of attention in the field of optical metrology and has shown great potential in solving various optical metrology tasks such as fringe analysis, phase unwrapping, and hologram reconstruction. For fringe analysis, current major works use U-Net and its derivatives as the backbone of the deep learning network, but suffer from a large number of model parameters and computational redundancy of the U-Net network, which outputs low-precision prediction results while taking up a lot of GPU memory. To solve these problems, compared with U-Net, a lightweight fringe analysis network with the size of only 1.7G is proposed to reduce the memory usage by over 70%, while improving the accuracy of phase retrieval by 10%, providing a new path for the widespread implementation in mobile devices of deep learning-based optical metrology.

Citation Download Citation

Yuxuan Che and Wei Yin "Fast 3D measurement based on fringe projection profilometry and deep learning", Proc. SPIE 12319, Optical Metrology and Inspection for Industrial Applications IX, 1231915 (21 December 2022); https://doi.org/10.1117/12.2642326

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