Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors
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 ex...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/1/293 |
| _version_ | 1798003624641560576 |
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| author | Yaxin Li Wenbin Li Shengjun Tang Walid Darwish Yuling Hu Wu Chen |
| author_facet | Yaxin Li Wenbin Li Shengjun Tang Walid Darwish Yuling Hu Wu Chen |
| author_sort | Yaxin Li |
| collection | DOAJ |
| description | 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 45−67 <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula>, is reduced to 4−6 min with an RGB-D sensor from 50−60 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. |
| first_indexed | 2024-04-11T12:12:00Z |
| format | Article |
| id | doaj.art-ac93e5f1c3ca448e930da0dbcab1469d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:12:00Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ac93e5f1c3ca448e930da0dbcab1469d2022-12-22T04:24:36ZengMDPI AGSensors1424-82202020-01-0120129310.3390/s20010293s20010293Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D SensorsYaxin Li0Wenbin Li1Shengjun Tang2Walid Darwish3Yuling Hu4Wu Chen5Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaGuangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) & Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518052, ChinaDepartment of Electronic and Informatics, Faculty of Engineering, Vrije Universiteit Brussel, 1050 Brussels, BelgiumDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaShenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, ChinaTo 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 45−67 <inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi mathvariant="normal">m</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula>, is reduced to 4−6 min with an RGB-D sensor from 50−60 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.https://www.mdpi.com/1424-8220/20/1/293as-built bimsautomaticrgb-d sensors |
| spellingShingle | Yaxin Li Wenbin Li Shengjun Tang Walid Darwish Yuling Hu Wu Chen Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors Sensors as-built bims automatic rgb-d sensors |
| title | Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors |
| title_full | Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors |
| title_fullStr | Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors |
| title_full_unstemmed | Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors |
| title_short | Automatic Indoor as-Built Building Information Models Generation by Using Low-Cost RGB-D Sensors |
| title_sort | automatic indoor as built building information models generation by using low cost rgb d sensors |
| topic | as-built bims automatic rgb-d sensors |
| url | https://www.mdpi.com/1424-8220/20/1/293 |
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