{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T19:39:59Z","timestamp":1783798799341,"version":"3.55.0"},"reference-count":123,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T00:00:00Z","timestamp":1660348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>The representation of the physical world is an issue that concerns the scientific community studying computer vision, more and more. Recently, research has focused on modern techniques and methods of photogrammetry and stereoscopy with the aim of reconstructing three-dimensional realistic models with high accuracy and metric information in a short time. In order to obtain data at a relatively low cost, various tools have been developed, such as depth cameras. RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. This survey aims to describe RGB-D camera technology. We discuss the hardware and data acquisition process, in both static and dynamic environments. Depth map sensing techniques are described, focusing on their features, pros, cons, and limitations; emerging challenges and open issues to investigate are analyzed; and some countermeasures are described. In addition, the advantages, disadvantages, and limitations of RGB-D cameras in all aspects are also described critically. This survey will be useful for researchers who want to acquire, process, and analyze the data collected.<\/jats:p>","DOI":"10.3390\/digital2030022","type":"journal-article","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T21:09:06Z","timestamp":1660511346000},"page":"401-421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["On 3D Reconstruction Using RGB-D Cameras"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2528-4295","authenticated-orcid":false,"given":"Kyriaki A.","family":"Tychola","sequence":"first","affiliation":[{"name":"MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0682-1750","authenticated-orcid":false,"given":"Ioannis","family":"Tsimperidis","sequence":"additional","affiliation":[{"name":"MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5545-1499","authenticated-orcid":false,"given":"George A.","family":"Papakostas","sequence":"additional","affiliation":[{"name":"MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Orts-Escolano, S., Rhemann, C., Fanello, S., Chang, W., Kowdle, A., Degtyarev, Y., Kim, D., Davidson, P.L., Khamis, S., and Dou, M. 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