{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T23:11:55Z","timestamp":1776985915778,"version":"3.51.4"},"reference-count":75,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,23]],"date-time":"2023-04-23T00:00:00Z","timestamp":1682208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Three-dimensional (3D) shape acquisition of objects from a single-shot image has been highly demanded by numerous applications in many fields, such as medical imaging, robotic navigation, virtual reality, and product in-line inspection. This paper presents a robust 3D shape reconstruction approach integrating a structured-light technique with a deep learning-based artificial neural network. The proposed approach employs a single-input dual-output network capable of transforming a single structured-light image into two intermediate outputs of multiple phase-shifted fringe patterns and a coarse phase map, through which the unwrapped true phase distributions containing the depth information of the imaging target can be accurately determined for subsequent 3D reconstruction process. A conventional fringe projection technique is employed to prepare the ground-truth training labels, and part of its classic algorithm is adopted to preserve the accuracy of the 3D reconstruction. Numerous experiments have been conducted to assess the proposed technique, and its robustness makes it a promising and much-needed tool for scientific research and engineering applications.<\/jats:p>","DOI":"10.3390\/s23094209","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T03:04:08Z","timestamp":1682305448000},"page":"4209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep Learning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5154-0125","authenticated-orcid":false,"given":"Andrew-Hieu","family":"Nguyen","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, The Catholic University of America, Washington, DC 20064, USA"},{"name":"Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6868-8994","authenticated-orcid":false,"given":"Khanh L.","family":"Ly","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Van Khanh","family":"Lam","sequence":"additional","affiliation":[{"name":"Sheikh Zayed Institute for Pediatric Surgical Innovation, Children\u2019s National Hospital, Washington, DC 20012, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6384-3107","authenticated-orcid":false,"given":"Zhaoyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, The Catholic University of America, Washington, DC 20064, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.optlaseng.2009.03.012","article-title":"Dynamic 3-D shape measurement method: A review","volume":"48","author":"Su","year":"2010","journal-title":"Opt. Lasers Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.culher.2009.02.006","article-title":"From 3D reconstruction to virtual reality: A complete methodology for digital archaeological exhibition","volume":"11","author":"Bruno","year":"2010","journal-title":"J. Cult. Herit."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huang, S., Xu, K., Li, M., and Wu, M. (2019). Improved Visual Inspection through 3D Image Reconstruction of Defects Based on the Photometric Stereo Technique. Sensors, 19.","DOI":"10.3390\/s19224970"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102011","DOI":"10.1016\/j.compmedimag.2021.102011","article-title":"Three-dimensional reconstruction of In Vivo human lumbar spine from biplanar radiographs","volume":"96","author":"Bennani","year":"2022","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_5","unstructured":"Do, P., and Nguyen, Q. (2019, January 25\u201327). A Review of Stereo-Photogrammetry Method for 3-D Reconstruction in Computer Vision. Proceedings of the 19th International Symposium on Communications and Information Technologies, Ho Chi Minh City, Vietnam."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1117\/1.1631921","article-title":"Review of 20 years of range sensor development","volume":"13","author":"Blais","year":"2004","journal-title":"J. Electron. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1117\/1.602438","article-title":"Overview of threedimensional shape measurement using optical methods","volume":"39","author":"Chen","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11007","DOI":"10.3390\/s130811007","article-title":"A Comparative Analysis between Active and Passive Techniques for Underwater 3D Reconstruction of Close-Range Objects","volume":"13","author":"Bianco","year":"2013","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khilar, R., Chitrakala, S., and Selvamparvathy, S. (2013, January 2\u20133). 3D image reconstruction: Techniques, applications and challenges. Proceedings of the 2013 International Conference on Optical Imaging Sensor and Security, Coimbatore, India.","DOI":"10.1109\/ICOISS.2013.6678395"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.optlaseng.2018.02.017","article-title":"High-speed 3D shape measurement with structured light methods: A review","volume":"106","author":"Zhang","year":"2018","journal-title":"Opt. Lasers Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5134","DOI":"10.1364\/AO.426189","article-title":"MIMONet: Structured-light 3D shape reconstruction by a multi-input multi-output network","volume":"60","author":"Nguyen","year":"2021","journal-title":"Appl. Opt."},{"key":"ref_12","first-page":"355","article-title":"Pose estimation and map building with a Time-Of-Flight-camera for robot navigation","volume":"5","author":"Prusak","year":"2008","journal-title":"Int. J. Intell. Syst. Technol. Appl."},{"key":"ref_13","unstructured":"Kolb, A., Barth, E., Koch, R., and Larsen, R. (April, January 30). Time-of-Flight Sensors in Computer Graphics. Proceedings of the Eurographics 2009\u2014State of the Art Reports, Munich, Germany."},{"key":"ref_14","unstructured":"Kahn, S., Wuest, H., and Fellner, D. (2010, January 17\u201321). Time-of-flight based Scene Reconstruction with a Mesh Processing Tool for Model based Camera Tracking. Proceedings of the International Conference on Computer Vision Theory and Applications\u2014Volume 1: VISAPP, Angers, France."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, D., and Lee, S. (2012, January 26\u201329). Advances in 3D Camera: Time-of-Flight vs. Active Triangulation. Proceedings of the Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, Jeju Island, Republic of Korea.","DOI":"10.1007\/978-3-642-33926-4_28"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1364\/AOP.3.000128","article-title":"Structured-light 3D surface imaging: A tutorial","volume":"3","author":"Geng","year":"2011","journal-title":"Adv. Opt. Photonics"},{"key":"ref_17","first-page":"18","article-title":"Real-time structured light profilometry: A review","volume":"87","author":"Jeught","year":"2016","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fernandez, S., Salvi, J., and Pribanic, T. (2010, January 13\u201318). Absolute phase mapping for one-shot dense pattern projection. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), San Francisco, CA, USA.","DOI":"10.1109\/CVPRW.2010.5543483"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Moreno, D., and Taubin, G. (2012, January 13\u201315). Simple, Accurate, and Robust Projector-Camera Calibration. Proceedings of the 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission, Zurich, Switzerland.","DOI":"10.1109\/3DIMPVT.2012.77"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jensen, J., Hannemose, M., B\u00e6rentzen, A., Wilm, J., Frisvad, J., and Dahl, A. (2021). Surface Reconstruction from Structured Light Images Using Differentiable Rendering. Sensors, 21.","DOI":"10.3390\/s21041068"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8659847","DOI":"10.1155\/2018\/8659847","article-title":"A Structured Light RGB-D Camera System for Accurate Depth Measurement","volume":"2018","author":"Tran","year":"2018","journal-title":"Int. J. Opt."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Diba, A., Sharma, V., Pazandeh, A., Pirsiavash, H., and Gool, L. (2017, January 21\u201326). Weakly Supervised Cascaded Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.545"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9651","DOI":"10.1007\/s11042-017-5349-7","article-title":"Adaptable deep learning structures for object labeling\/tracking under dynamic visual environments","volume":"77","author":"Doulamis","year":"2018","journal-title":"Multimed. Tools. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Toshev, A., and Szegedy, C. (2014, January 23\u201328). DeepPose: Human Pose Estimation via Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.214"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/s11263-015-0876-z","article-title":"A Deep Structured Model with Radius\u2013Margin Bound for 3D Human Activity Recognition","volume":"118","author":"Lin","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1155\/2018\/7068349","article-title":"Deep Learning for Computer Vision: A Brief Review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Velasco-Hernandez, G., Krpalkova, L., Riordan, D., and Walsh, J. (2019, January 2\u20133). Deep Learning vs. Traditional Computer Vision. Proceedings of the 2019 Computer Vision Conference (CVC), Las Vegas, NV, USA.","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1109\/TPAMI.2019.2954885","article-title":"Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era","volume":"43","author":"Han","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s11042-020-09722-8","article-title":"Single image 3D object reconstruction based on deep learning: A review","volume":"80","author":"Fu","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"57539","DOI":"10.1109\/ACCESS.2019.2914150","article-title":"RealPoint3D: An Efficient Generation Network for 3D Object Reconstruction From a Single Image","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"17091","DOI":"10.1364\/OE.27.017091","article-title":"Deep neural networks for single shot structured light profilometry","volume":"27","author":"Jeught","year":"2019","journal-title":"Opt. Express"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110837","DOI":"10.1016\/j.measurement.2022.110837","article-title":"Three-dimensional measurement of precise shaft parts based on line structured light and deep learning","volume":"191","author":"Yang","year":"2022","journal-title":"Measurement"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"061407","DOI":"10.1117\/1.OE.61.6.061407","article-title":"Defect detection method for specular surfaces based on deflectometry and deep learning","volume":"61","author":"Guan","year":"2022","journal-title":"Opt. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"34656","DOI":"10.1364\/OE.438444","article-title":"Hybrid-net: A two-to-one deep learning framework for three-wavelength phase-shifting interferometry","volume":"29","author":"Li","year":"2021","journal-title":"Opt. Express"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.optcom.2018.12.058","article-title":"Fringe pattern denoising based on deep learning","volume":"437","author":"Yan","year":"2019","journal-title":"Opt. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106639","DOI":"10.1016\/j.optlaseng.2021.106639","article-title":"Three-dimensional Shape Reconstruction from Single-shot Speckle Image Using Deep Convolutional Neural Networks","volume":"143","author":"Nguyen","year":"2021","journal-title":"Opt. Lasers Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s11801-022-2082-x","article-title":"Wavelet based deep learning for depth estimation from single fringe pattern of fringe projection profilometry","volume":"18","author":"Zhu","year":"2022","journal-title":"Optoelectron. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8024","DOI":"10.1364\/OE.418430","article-title":"Single-shot fringe projection profilometry based on deep learning and computer graphics","volume":"29","author":"Wang","year":"2021","journal-title":"Opt. Express"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"025202","DOI":"10.1088\/1361-6501\/ac329d","article-title":"Depth measurement based on a convolutional neural network and structured light","volume":"33","author":"Jia","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103023","DOI":"10.1016\/j.cviu.2020.103023","article-title":"End-to-end deep learning-based fringe projection framework for 3D profiling of objects","volume":"199","author":"Machineni","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"32547","DOI":"10.1364\/OE.435606","article-title":"Unsupervised deep learning for 3D reconstruction with dual-frequency fringe projection profilometry","volume":"29","author":"Fan","year":"2021","journal-title":"Opt. Express"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s13634-022-00848-5","article-title":"3D reconstruction from structured-light profilometry with dual-path hybrid network","volume":"2022","author":"Wang","year":"2022","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"10105","DOI":"10.1364\/AO.468984","article-title":"Different structured-light patterns in single-shot 2D-to-3D image conversion using deep learning","volume":"61","author":"Nguyen","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Wang, Y., and Wang, Z. (2020). Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks. Sensors, 20.","DOI":"10.3390\/s20133718"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"36568","DOI":"10.1364\/OE.410428","article-title":"Fringe projection profilometry by conducting deep learning from its digital twin","volume":"28","author":"Zheng","year":"2020","journal-title":"Opt. Express"},{"key":"ref_46","first-page":"114101","article-title":"Single-shot structured light projection profilometry with SwinConvUNet","volume":"61","author":"Wang","year":"2022","journal-title":"Opt. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7100","DOI":"10.1364\/AO.58.007100","article-title":"Real-time 3D shape measurement using 3LCD projection and deep machine learning","volume":"58","author":"Nguyen","year":"2019","journal-title":"Apt. Opt."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"107483","DOI":"10.1016\/j.optlaseng.2023.107483","article-title":"Untrained deep learning-based phase retrieval for fringe projection profilometry","volume":"164","author":"Yu","year":"2023","journal-title":"Opt. Lasers Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, Y., Ji, Y., Qian, J., Che, Y., Zuo, C., Chen, Q., and Feng, S. (2022). Deep Learning-Based 3D Measurements with Near-Infrared Fringe Projection. Sensors, 22.","DOI":"10.3390\/s22176469"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1080\/09500340.2022.2101701","article-title":"Single-shot 3D shape reconstruction for complex surface objects with colour texture based on deep learning","volume":"69","author":"Xu","year":"2022","journal-title":"J. Mod. Opt."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"9405","DOI":"10.1364\/OE.387215","article-title":"Dynamic 3-D measurement based on fringe-to-fringe transformation using deep learning","volume":"28","author":"Yu","year":"2020","journal-title":"Opt. Express"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"106628","DOI":"10.1016\/j.optlaseng.2021.106628","article-title":"Phase error compensation based on Tree-Net using deep learning","volume":"143","author":"Yang","year":"2021","journal-title":"Opt. Lasers Eng."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Nguyen, H., and Wang, Z. (2021). Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network. Photonics, 8.","DOI":"10.3390\/photonics8110459"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8589","DOI":"10.1364\/AO.470208","article-title":"Single-shot 3D shape acquisition using a learning-based structured-light technique","volume":"61","author":"Nguyen","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"025001","DOI":"10.1117\/1.AP.1.2.025001","article-title":"Fringe pattern analysis using deep learning","volume":"1","author":"Feng","year":"2019","journal-title":"Adv. Photonics"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3424","DOI":"10.1364\/OE.449468","article-title":"Composite fringe projection deep learning profilometry for single-shot absolute 3D shape measurement","volume":"30","author":"Li","year":"2022","journal-title":"Opt. Express"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"128323","DOI":"10.1016\/j.optcom.2022.128323","article-title":"Single-shot high-precision 3D reconstruction with color fringe projection profilometry based BP neural network","volume":"517","author":"Zhang","year":"2022","journal-title":"Opt. Commun."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"110663","DOI":"10.1016\/j.measurement.2021.110663","article-title":"Accurate 3D reconstruction via fringe-to-phase network","volume":"190","author":"Nguyen","year":"2022","journal-title":"Measurement"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liang, J., Zhang, J., Shao, J., Song, B., Yao, B., and Liang, R. (2020). Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging. Sensors, 20.","DOI":"10.3390\/s20133691"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"28929","DOI":"10.1364\/OE.27.028929","article-title":"Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement","volume":"27","author":"Shi","year":"2019","journal-title":"Opt. Express"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"20175","DOI":"10.1038\/s41598-019-56222-3","article-title":"Temporal phase unwrapping using deep learning","volume":"9","author":"Yin","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1364\/OL.388994","article-title":"Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry","volume":"45","author":"Qian","year":"2020","journal-title":"Opt. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"016102","DOI":"10.1063\/5.0069386","article-title":"Untrained deep learning-based fringe projection profilometry","volume":"7","author":"Yu","year":"2022","journal-title":"APL Photonics"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"106623","DOI":"10.1016\/j.optlaseng.2021.106623","article-title":"A multi-code 3D measurement technique based on deep learning","volume":"143","author":"Yao","year":"2021","journal-title":"Opt. Lasers Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"064104","DOI":"10.1117\/1.OE.60.6.064104","article-title":"Absolute phase retrieval for a single-shot fringe projection profilometry based on deep learning","volume":"60","author":"Li","year":"2021","journal-title":"Opt. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"101171","DOI":"10.1016\/j.gmod.2023.101171","article-title":"Learning-based 3D imaging from single structured-light image","volume":"126","author":"Nguyen","year":"2023","journal-title":"Graph. Models"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"A9","DOI":"10.1364\/AO.54.0000A9","article-title":"Real-time, high-accuracy 3D imaging and shape measurement","volume":"54","author":"Nguyen","year":"2015","journal-title":"Appl. Opt."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"014004","DOI":"10.1088\/2515-7647\/abcbe4","article-title":"Accuracy assessment of fringe projection profilometry and digital image correlation techniques for three-dimensional shape measurements","volume":"3","author":"Nguyen","year":"2021","journal-title":"J. Phys. Photonics"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"056009","DOI":"10.1117\/1.JBO.23.5.056009","article-title":"Demonstration of a laparoscopic structured-illumination three-dimensional imaging system for guiding reconstructive bowel anastomosis","volume":"23","author":"Le","year":"2018","journal-title":"J. Biomed. Opt."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1364\/OL.32.002438","article-title":"Three-dimensional shape measurement with an arbitrarily arranged fringe projection profilometry system","volume":"32","author":"Du","year":"2007","journal-title":"Opt. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"16926","DOI":"10.1364\/OE.20.016926","article-title":"Hyper-accurate flexible calibration technique for fringe-projection-based three-dimensional imaging","volume":"20","author":"Vo","year":"2012","journal-title":"Opt. Express"},{"key":"ref_72","unstructured":"(2023, April 13). Single-Input Dual-Output 3D Shape Reconstruction. Available online: https:\/\/figshare.com\/s\/c09f17ba357d040331e4."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_74","unstructured":"Keras (2023, April 13). ExponentialDecay. Available online: https:\/\/keras.io\/api\/optimizers\/learning_rate_schedules\/."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"100104","DOI":"10.1016\/j.rio.2021.100104","article-title":"hNet: Single-shot 3D shape reconstruction using structured light and h-shaped global guidance network","volume":"4","author":"Nguyen","year":"2021","journal-title":"Results Opt."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4209\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:21:38Z","timestamp":1760124098000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,23]]},"references-count":75,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094209"],"URL":"https:\/\/doi.org\/10.3390\/s23094209","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,23]]}}}