{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T08:54:18Z","timestamp":1770454458910,"version":"3.49.0"},"reference-count":21,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013494","name":"West Light Foundation of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["XAB2017B20"],"award-info":[{"award-number":["XAB2017B20"]}],"id":[{"id":"10.13039\/501100013494","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201804910323"],"award-info":[{"award-number":["201804910323"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61505246"],"award-info":[{"award-number":["61505246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1918260"],"award-info":[{"award-number":["1918260"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.<\/jats:p>","DOI":"10.3390\/s20133691","type":"journal-article","created":{"date-parts":[[2020,7,2]],"date-time":"2020-07-02T02:44:25Z","timestamp":1593657865000},"page":"3691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0324-1497","authenticated-orcid":false,"given":"Jian","family":"Liang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Transient Optics and Photonics, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA"}]},{"given":"Junchao","family":"Zhang","sequence":"additional","affiliation":[{"name":"James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1253-4096","authenticated-orcid":false,"given":"Jianbo","family":"Shao","sequence":"additional","affiliation":[{"name":"James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA"}]},{"given":"Bofan","family":"Song","sequence":"additional","affiliation":[{"name":"James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1723-6680","authenticated-orcid":false,"given":"Baoli","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transient Optics and Photonics, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}]},{"given":"Rongguang","family":"Liang","sequence":"additional","affiliation":[{"name":"James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.optlaseng.2018.02.017","article-title":"High-speed 3D shape meansurement with structured light methods: A review","volume":"106","author":"Zhang","year":"2018","journal-title":"Opt. 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