AI-powered 3D shape measurement through a phase-to-phase network

Beiwen Li; Vignesh Suresh

doi:10.1117/12.2622761

30 May 2022 AI-powered 3D shape measurement through a phase-to-phase network

Beiwen Li, Vignesh Suresh

Author Affiliations +

Proceedings Volume PC12098, Dimensional Optical Metrology and Inspection for Practical Applications XI; PC1209804 (2022) https://doi.org/10.1117/12.2622761
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Recently, researchers have found that the power of deep learning can be leveraged to perform high quality 3D shape measurement by directly learning from a single-shot fringe image. However, such end-to-end fringe-to-depth learning has limited flexibility given that its trained deep neural network can only be used for patterns with a certain frequency. This research proposes a phase-to-phase learning approach to address such limitation. By establishing a phase-to-phase training network from phase obtained from Fourier transform to phase obtained from phase shifting, this proposed network can be flexibly applied to measurements with fringe images of different pattern frequencies.

Conference Presentation

Citation Download Citation

Beiwen Li and Vignesh Suresh "AI-powered 3D shape measurement through a phase-to-phase network", Proc. SPIE PC12098, Dimensional Optical Metrology and Inspection for Practical Applications XI, PC1209804 (30 May 2022); https://doi.org/10.1117/12.2622761

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