{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:29:56Z","timestamp":1771957796958,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000183","name":"United States Army Research Office","doi-asserted-by":"publisher","award":["W911NF-23-1-0367"],"award-info":[{"award-number":["W911NF-23-1-0367"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The field of computer vision has been focusing on achieving accurate three-dimensional (3D) object representations from a single two-dimensional (2D) image through deep artificial neural networks. Recent advancements in 3D shape reconstruction techniques that combine structured light and deep learning show promise in acquiring high-quality geometric information about object surfaces. This paper introduces a new single-shot 3D shape reconstruction method that uses a nonlinear fringe transformation approach through both supervised and unsupervised learning networks. In this method, a deep learning network learns to convert a grayscale fringe input into multiple phase-shifted fringe outputs with different frequencies, which act as an intermediate result for the subsequent 3D reconstruction process using the structured-light fringe projection profilometry technique. Experiments have been conducted to validate the practicality and robustness of the proposed technique. The experimental results demonstrate that the unsupervised learning approach using a deep convolutional generative adversarial network (DCGAN) is superior to the supervised learning approach using UNet in image-to-image generation. The proposed technique\u2019s ability to accurately reconstruct 3D shapes of objects using only a single fringe image opens up vast opportunities for its application across diverse real-world scenarios.<\/jats:p>","DOI":"10.3390\/s24103246","type":"journal-article","created":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T11:06:41Z","timestamp":1716203201000},"page":"3246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Single-Shot 3D Reconstruction via Nonlinear Fringe Transformation: Supervised and Unsupervised Learning Approaches"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5154-0125","authenticated-orcid":false,"given":"Andrew-Hieu","family":"Nguyen","sequence":"first","affiliation":[{"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\/0000-0002-6384-3107","authenticated-orcid":false,"given":"Zhaoyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, School of Engineering, The Catholic University of America, Washington, DC 20064, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2289","DOI":"10.1109\/TPAMI.2012.58","article-title":"A New In-Camera Imaging Model for Color Computer Vision and Its Application","volume":"34","author":"Kim","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/TPAMI.2005.80","article-title":"Geometric and algebraic constraints of projected concentric circles and their applications to camera calibration","volume":"27","author":"Kim","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1109\/JPROC.2008.928765","article-title":"Smart Camera Based Monitoring System and Its Application to Assisted Living","volume":"96","author":"Fleck","year":"2008","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/MSP.2003.1203211","article-title":"Computer vision applied to super resolution","volume":"20","author":"Capel","year":"2003","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1111\/j.1467-8659.2009.01583.x","article-title":"Computer vision applied to super resolution","volume":"29","author":"Kolb","year":"2010","journal-title":"Comput. Graph. Forum"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.optlaseng.2014.04.002","article-title":"Digital image correlation in experimental mechanics and image registration in computer vision: Similarities, differences and complements","volume":"65","author":"Wang","year":"2015","journal-title":"Opt. Lasers Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9030","DOI":"10.1364\/AO.56.009030","article-title":"Accurate 3D shape measurement of multiple separate objects with stereo vision","volume":"56","author":"Nguyen","year":"2017","journal-title":"Appl. Opt."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019 photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"G44","DOI":"10.1364\/AO.53.000G44","article-title":"Recent advances in digital holography [invited]","volume":"53","author":"Osten","year":"2014","journal-title":"Appl. Opt."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1109\/TIP.2013.2237921","article-title":"Accurate Multiple View 3D Reconstruction Using Patch-Based Stereo for Large-Scale Scenes","volume":"22","author":"Shen","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"04019027","DOI":"10.1061\/(ASCE)CP.1943-5487.0000842","article-title":"Deep Learning Approach to Point Cloud Scene Understanding for Automated Scan to 3D Reconstruction","volume":"33","author":"Chen","year":"2019","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neucom.2015.08.127","article-title":"Deep Learning Representation using Autoencoder for 3D Shape Retrieval","volume":"204","author":"Zhu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1038\/s42256-020-00273-z","article-title":"Deep learning for tomographic image reconstruction","volume":"2","author":"Wang","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41377-022-00714-x","article-title":"Deep learning in optical metrology: A review","volume":"11","author":"Zuo","year":"2022","journal-title":"Light Sci. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23173","DOI":"10.1364\/OE.27.023173","article-title":"Rapid and robust two-dimensional phase unwrapping via deep learning","volume":"27","author":"Zhang","year":"2019","journal-title":"Opt. Express"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1109\/TSM.2018.2849206","article-title":"A Computer Vision-Inspired Deep Learning Architecture for Virtual Metrology Modeling with 2-Dimensional Data","volume":"31","author":"Maggipinto","year":"2018","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4271","DOI":"10.1007\/s00170-022-09084-5","article-title":"Optical metrology for digital manufacturing: A review","volume":"120","author":"Catalucci","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"117474","DOI":"10.1016\/j.jmatprotec.2021.117474","article-title":"Deep DIC: Deep learning-based digital image correlation for end-to-end displacement and strain measurement","volume":"302","author":"Yang","year":"2022","journal-title":"J. Mater. Process. Technol."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1364\/PRJ.420944","article-title":"Generalized framework for non-sinusoidal fringe analysis using deep learning","volume":"9","author":"Feng","year":"2021","journal-title":"Photonics Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.optcom.2018.12.058","article-title":"Generalized framework for non-sinusoidal fringe analysis using deep learning","volume":"437","author":"Yan","year":"2019","journal-title":"Opt. Comm."},{"key":"ref_25","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_26","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_27","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_28","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_29","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":"Appl. Opt."},{"key":"ref_30","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_31","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_32","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_33","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."},{"key":"ref_34","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_35","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_36","doi-asserted-by":"crossref","first-page":"33287","DOI":"10.1364\/OE.501067","article-title":"Deep learning-based end-to-end 3D depth recovery from a single-frame fringe pattern with the MSUNet++ network","volume":"31","author":"Wang","year":"2023","journal-title":"Opt. Express"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"085402","DOI":"10.1088\/1361-6501\/acd136","article-title":"PCTNet: Depth estimation from single structured light image with a parallel CNN-transformer network","volume":"34","author":"Zhu","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"045203","DOI":"10.1088\/1361-6501\/ad1c48","article-title":"Depth acquisition from dual-frequency fringes based on end-to-end learning","volume":"35","author":"Wu","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1364\/OE.505544","article-title":"Dual-stage hybrid network for single-shot fringe projection profilometry based on a phase-height model","volume":"32","author":"Song","year":"2024","journal-title":"Opt. Express"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3215","DOI":"10.1364\/AO.483303","article-title":"LiteF2DNet: A lightweight learning framework for 3D reconstruction using fringe projection profilometry","volume":"62","author":"Ravi","year":"2023","journal-title":"Appl. Opt."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9144","DOI":"10.1364\/AO.504023","article-title":"Depth estimation from a single-shot fringe pattern based on DD-Inceptionv2-UNet","volume":"62","author":"Wang","year":"2023","journal-title":"Appl. Opt."},{"key":"ref_42","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":"Zhao","year":"2021","journal-title":"Opt. Lasers Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.optlaseng.2019.04.020","article-title":"Micro deep learning profilometry for high-speed 3D surface imaging","volume":"121","author":"Feng","year":"2019","journal-title":"Opt. Lasers Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"170727","DOI":"10.1016\/j.ijleo.2023.170727","article-title":"A novel phase unwrapping method for binocular structured light 3D reconstruction based on deep learning","volume":"279","author":"Liu","year":"2023","journal-title":"Optik"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Nguyen, A., and Wang, Z. (2023). Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning. Sensors, 23.","DOI":"10.3390\/s23167284"},{"key":"ref_46","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_47","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_48","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_49","doi-asserted-by":"crossref","unstructured":"Qi, Z., Liu, X., Pang, J., Hao, Y., Hu, R., and Zhang, Y. (2023). PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm. Sensors, 23.","DOI":"10.3390\/s23198305"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1364\/AO.443685","article-title":"Virtual temporal phase-shifting phase extraction using generative adversarial networks","volume":"61","author":"Yan","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"107866","DOI":"10.1016\/j.optlaseng.2023.107866","article-title":"Deep learning-based binocular composite color fringe projection profilometry for fast 3D measurements","volume":"172","author":"Fu","year":"2024","journal-title":"Opt. Lasers Eng."},{"key":"ref_52","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_53","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_54","doi-asserted-by":"crossref","first-page":"109842","DOI":"10.1016\/j.optlastec.2023.109842","article-title":"Color phase order coding and interleaved phase unwrapping for three-dimensional shape measurement with few projected pattern","volume":"168","author":"Yu","year":"2024","journal-title":"Opt. Laser Technol."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","unstructured":"Hu, W., Miao, H., Yan, K., and Fu, Y. (2021). A Fringe Phase Extraction Method Based on Neural Network. Sensors, 21.","DOI":"10.3390\/s21051664"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"14965","DOI":"10.1364\/OE.487917","article-title":"Single-shot 3D measurement of highly reflective objects with deep learning","volume":"31","author":"Wang","year":"2023","journal-title":"Opt. Express"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sun, G., Li, B., Li, Z., Wang, X., Cai, P., and Qie, C. (2023). Phase unwrapping based on channel transformer U-Net for single-shot fringe projection profilometry. J. Opt., 1\u201311.","DOI":"10.1007\/s12596-023-01515-0"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"113323","DOI":"10.1016\/j.measurement.2023.113323","article-title":"Pixel-wise phase unwrapping of fringe projection profilometry based on deep learning","volume":"220","author":"Huang","year":"2023","journal-title":"Measurement"},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"128008","DOI":"10.1016\/j.optcom.2022.128008","article-title":"Deep absolute phase recovery from single-frequency phase map for handheld 3D measurement","volume":"512","author":"Bai","year":"2022","journal-title":"Opt. Comm."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/TIP.2022.3230245","article-title":"Super-Resolution Phase Retrieval Network for Single-Pattern Structured Light 3D Imaging","volume":"32","author":"Song","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_63","first-page":"7910","article-title":"Triple-output phase unwrapping network with a physical prior in fringe projection profilometry","volume":"62","author":"Zhu","year":"2023","journal-title":"Opt. Express"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Ly, K., Lam, V., and Wang, Z. (2023). Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep Learning. Sensors, 23.","DOI":"10.3390\/s23094209"},{"key":"ref_65","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_66","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_67","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_68","doi-asserted-by":"crossref","first-page":"035203","DOI":"10.1088\/1361-6501\/ad1321","article-title":"A Y-shaped network based single-shot absolute phase recovery method for fringe projection profilometry","volume":"35","author":"Tan","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"230034","DOI":"10.29026\/oea.2024.230034","article-title":"Physics-informed deep learning for fringe pattern analysis","volume":"7","author":"Yin","year":"2024","journal-title":"Opto-Electron. Adv."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/10\/3246\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:45:16Z","timestamp":1760107516000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/10\/3246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,20]]},"references-count":71,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24103246"],"URL":"https:\/\/doi.org\/10.3390\/s24103246","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,20]]}}}