{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:19:46Z","timestamp":1783610386349,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,4]],"date-time":"2020-01-04T00:00:00Z","timestamp":1578096000000},"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>To generate indoor as-built building information models (AB BIMs) automatically and economically is a great technological challenge. Many approaches have been developed to address this problem in recent years, but it is far from being settled, particularly for the point cloud segmentation and the extraction of the relationship among different elements due to the complicated indoor environment. This is even more difficult for the low-quality point cloud generated by low-cost scanning equipment. This paper proposes an automatic as-built BIMs generation framework that transforms the noisy 3D point cloud produced by a low-cost RGB-D sensor (about 708 USD for data collection equipment, 379 USD for the Structure sensor and 329 USD for iPad) to the as-built BIMs, without any manual intervention. The experiment results show that the proposed method has competitive robustness and accuracy, compared to the high-quality Terrestrial Lidar System (TLS), with the element extraction accuracy of 100%, mean dimension reconstruction accuracy of 98.6% and mean area reconstruction accuracy of 93.6%. Also, the proposed framework makes the BIM generation workflows more efficient in both data collection and data processing. In the experiments, the time consumption of data collection for a typical room, with an area of 45\u201367      m 2     , is reduced to 4\u20136 min with an RGB-D sensor from 50\u201360 min with TLS. The processing time to generate BIM models is about half minutes automatically, from around 10 min with a conventional semi-manual method.<\/jats:p>","DOI":"10.3390\/s20010293","type":"journal-article","created":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T03:48:48Z","timestamp":1578282528000},"page":"293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5610-3223","authenticated-orcid":false,"given":"Yaxin","family":"Li","sequence":"first","affiliation":[{"name":"Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, China"},{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengjun","family":"Tang","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Urban Informatics &amp; Shenzhen Key Laboratory of Spatial Smart Sensing and Services &amp; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) &amp; Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0549-6052","authenticated-orcid":false,"given":"Walid","family":"Darwish","sequence":"additional","affiliation":[{"name":"Department of Electronic and Informatics, Faculty of Engineering, Vrije Universiteit Brussel, 1050 Brussels, Belgium"},{"name":"Geomatics Engineering Lab, Civil Engineering Department, Faculty of Engineering, Cairo University, Cairo 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuling","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wu","family":"Chen","sequence":"additional","affiliation":[{"name":"Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, China"},{"name":"Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.autcon.2013.10.023","article-title":"Building Information Modeling (BIM) for existing buildings\u2014Literature review and future needs","volume":"38","author":"Volk","year":"2014","journal-title":"Autom. 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