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J. Imaging
Volume 10
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10.3390/jimaging10080179
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Article

Deep Learning for Single-Shot Structured Light Profilometry: A Comprehensive Dataset and Performance Analysis

by
Rhys G. Evans
1,*,
Ester Devlieghere
2,†,
Robrecht Keijzer
2,†,
Joris J. J. Dirckx
2 and
Sam Van der Jeught
1
1
Industrial Vision Lab (InViLab), Faculty of Applied Engineering, Campus Groenenborger, University of Antwerp, Groenenborgerlaan 179, 2020 Antwerp, Belgium
2
Laboratory of Biomedical Physics (BIMEF), University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Imaging 2024, 10(8), 179; https://doi.org/10.3390/jimaging10080179
Submission received: 28 June 2024 / Revised: 18 July 2024 / Accepted: 22 July 2024 / Published: 24 July 2024
(This article belongs to the Special Issue Deep Learning in Computer Vision)
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Abstract

In 3D optical metrology, single-shot deep learning-based structured light profilometry (SS-DL-SLP) has gained attention because of its measurement speed, simplicity of optical setup, and robustness to noise and motion artefacts. However, gathering a sufficiently large training dataset for these techniques remains challenging because of practical limitations. This paper presents a comprehensive DL-SLP dataset of over 10,000 physical data couples. The dataset was constructed by 3D-printing a calibration target featuring randomly varying surface profiles and storing the height profiles and the corresponding deformed fringe patterns. Our dataset aims to serve as a benchmark for evaluating and comparing different models and network architectures in DL-SLP. We performed an analysis of several established neural networks, demonstrating high accuracy in obtaining full-field height information from previously unseen fringe patterns. In addition, the network was validated on unique objects to test the overall robustness of the trained model. To facilitate further research and promote reproducibility, all code and the dataset are made publicly available. This dataset will enable researchers to explore, develop, and benchmark novel DL-based approaches for SS-DL-SLP.
Keywords: 3D imaging and sensing; machine learning; profilometry; structured light; dataset 3D imaging and sensing; machine learning; profilometry; structured light; dataset

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MDPI and ACS Style

Evans, R.G.; Devlieghere, E.; Keijzer, R.; Dirckx, J.J.J.; Van der Jeught, S. Deep Learning for Single-Shot Structured Light Profilometry: A Comprehensive Dataset and Performance Analysis. J. Imaging 2024, 10, 179. https://doi.org/10.3390/jimaging10080179

AMA Style

Evans RG, Devlieghere E, Keijzer R, Dirckx JJJ, Van der Jeught S. Deep Learning for Single-Shot Structured Light Profilometry: A Comprehensive Dataset and Performance Analysis. Journal of Imaging. 2024; 10(8):179. https://doi.org/10.3390/jimaging10080179

Chicago/Turabian Style

Evans, Rhys G., Ester Devlieghere, Robrecht Keijzer, Joris J. J. Dirckx, and Sam Van der Jeught. 2024. "Deep Learning for Single-Shot Structured Light Profilometry: A Comprehensive Dataset and Performance Analysis" Journal of Imaging 10, no. 8: 179. https://doi.org/10.3390/jimaging10080179

APA Style

Evans, R. G., Devlieghere, E., Keijzer, R., Dirckx, J. J. J., & Van der Jeught, S. (2024). Deep Learning for Single-Shot Structured Light Profilometry: A Comprehensive Dataset and Performance Analysis. Journal of Imaging, 10(8), 179. https://doi.org/10.3390/jimaging10080179

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