Stereo phase unwrapping using deep learning for single-shot absolute 3D shape measurement

Jiaming Qian; Shijie Feng; Yixuan Li; Qian Chen; Chao Zuo

doi:10.1117/12.2606607

24 November 2021 Stereo phase unwrapping using deep learning for single-shot absolute 3D shape measurement

Jiaming Qian, Shijie Feng, Yixuan Li, Qian Chen, Chao Zuo

Proceedings Volume 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations; 120690X (2021) https://doi.org/10.1117/12.2606607
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

In fringe projection proﬁlometry (FPP), eﬃciently recovering the absolute phase has always been a great chal-lenge. The stereo phase unwrapping (SPU) technologies based on geometric constraints can eliminate phase ambiguity without projecting any additional patterns, which maximizes the eﬃciency of the retrieval of abso-lute phase. Inspired by recent successes of deep learning for phase analysis, we demonstrate that deep learning can be an eﬀective tool that organically uniﬁes phase retrieval, geometric constraints, and phase unwrapping into a comprehensive framework. Driven by extensive training dataset, the properly trained neural network can achieve high-quality phase retrieval and robust phase ambiguity removal from only single-frame projection. Experimental results demonstrate that compared with conventional SPU, our deep-learning-based approach can more eﬃciently and robustly unwrap the phase of dense fringe images in a larger measurement volume with fewer camera views.

Citation Download Citation

Jiaming Qian, Shijie Feng, Yixuan Li, Qian Chen, and Chao Zuo "Stereo phase unwrapping using deep learning for single-shot absolute 3D shape measurement", Proc. SPIE 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations, 120690X (24 November 2021); https://doi.org/10.1117/12.2606607

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