{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T04:49:24Z","timestamp":1768279764935,"version":"3.49.0"},"reference-count":85,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T00:00:00Z","timestamp":1692489600000},"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>In recent years, integrating structured light with deep learning has gained considerable attention in three-dimensional (3D) shape reconstruction due to its high precision and suitability for dynamic applications. While previous techniques primarily focus on processing in the spatial domain, this paper proposes a novel time-distributed approach for temporal structured-light 3D shape reconstruction using deep learning. The proposed approach utilizes an autoencoder network and time-distributed wrapper to convert multiple temporal fringe patterns into their corresponding numerators and denominators of the arctangent functions. Fringe projection profilometry (FPP), a well-known temporal structured-light technique, is employed to prepare high-quality ground truth and depict the 3D reconstruction process. Our experimental findings show that the time-distributed 3D reconstruction technique achieves comparable outcomes with the dual-frequency dataset (p = 0.014) and higher accuracy than the triple-frequency dataset (p = 1.029 \u00d7 10\u22129), according to non-parametric statistical tests. Moreover, the proposed approach\u2019s straightforward implementation of a single training network for multiple converters makes it more practical for scientific research and industrial applications.<\/jats:p>","DOI":"10.3390\/s23167284","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T01:49:34Z","timestamp":1692582574000},"page":"7284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection 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":"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, The Catholic University of America, Washington, DC 20064, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,20]]},"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. 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