{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T21:48:19Z","timestamp":1773956899566,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71601013"],"award-info":[{"award-number":["71601013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4202017"],"award-info":[{"award-number":["4202017"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Youth Talent Support Program of Beijing Municipal Education Commission","award":["CIT&TCD201904050"],"award-info":[{"award-number":["CIT&TCD201904050"]}]},{"name":"the Fundamental Research Funds for BUCEA","award":["X20039"],"award-info":[{"award-number":["X20039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Indoor maps lay the foundation for most indoor location-based services (LBS). Building Information Modeling (BIM) data contains multiple dimensional computer-aided design information. Some studies have utilized BIM data to automatically extract 3D indoor maps. A complete 3D indoor map consists of both floor-level maps and cross-floor paths. Currently, the floor-level indoor maps are mainly either grid-based maps or topological maps, and the cross-floor path generation schemes are not adaptive to building elements with irregular 3D shapes. To address these issues, this study proposes a novel scheme to extract an accurate 3D indoor map with any shape using BIM data. Firstly, this study extracts grid-based maps from BIM data and generates the topological maps directly through the grid-based maps using image thinning. A novel hybrid indoor map, termed Grid-Topological map, is then formed by the grid-based maps and topological maps jointly. Secondly, this study obtains the cross-floor paths from cross-floor building elements by a four-step process, namely X-Z projection, boundary extraction, X-Z topological path generation, and path-BIM intersection. Finally, experiments on eight typical types of cross-floor building elements and three multi-floor real-world buildings were conducted to prove the effectiveness of the proposed scheme, the average accuracy rates of the evaluated paths are higher than 88%. This study will advance the 3D indoor maps generation and inspire the application of indoor maps in indoor LBS, indoor robots, and 3D geographic information systems.<\/jats:p>","DOI":"10.3390\/ijgi10100700","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T22:42:18Z","timestamp":1634251338000},"page":"700","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Qi","family":"Qiu","sequence":"first","affiliation":[{"name":"Department of Engineering Management, School of Civil Engineering, Central South University, Changsha 410075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Engineering Management, School of Civil Engineering, Central South University, Changsha 410075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingsheng","family":"Xie","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjun","family":"Han","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5278-6576","authenticated-orcid":false,"given":"Xiaoping","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, J., Luo, J., Hou, J., Wen, D., Feng, G., and Zhang, X. 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