Semantics-guided spatial data generation for complex large-scale indoor map from 3D colorized point clouds
With the popularization of the concept of smart cities and the development of indoor positioning and navigation services, as well as the increasing complexity of building structures with the continuous advancement of urbanization, automatic mapping of large-scale indoor spatial data has become the f...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-G-2025/961/2025/isprs-annals-X-G-2025-961-2025.pdf |
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| _version_ | 1839629752769445888 |
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| author | Z. Wang X. Wang J. Yang T. Li |
| author_facet | Z. Wang X. Wang J. Yang T. Li |
| author_sort | Z. Wang |
| collection | DOAJ |
| description | With the popularization of the concept of smart cities and the development of indoor positioning and navigation services, as well as the increasing complexity of building structures with the continuous advancement of urbanization, automatic mapping of large-scale indoor spatial data has become the fundamental work for subsequent real-life 3D applications. Therefore, this paper develops a semantics-guided generation method of indoor spatial data for mapping indoor spaces, where the roles of semantics are investigated for the subdivision and reconstruction of indoor spaces. It consists of the following four parts: (1) Semantic segmentation of 3D indoor scene; (2) Storey segmentation using semantics-enhanced height histogram; (3) Semantics-guided room segmentation based on building physical structures; (4) Room-wise boundary optimization using semantics-aware Recursive Search. Both quantitative and qualitative experiments are conducted on two public benchmark datasets: Stanford Large-Scale 3D Indoor Spaces (S3DIS) dataset and Matterport3D dataset. The results demonstrated that our method is capable of reconstructing complicated large-scale indoor scenes with higher robustness and reliability, outperforming existing state-of-the-art algorithms. |
| format | Article |
| id | doaj-art-9b37c0c80c99407c8e9eca828d21d8f5 |
| institution | Matheson Library |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-9b37c0c80c99407c8e9eca828d21d8f52025-07-14T20:08:08ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502025-07-01X-G-202596196910.5194/isprs-annals-X-G-2025-961-2025Semantics-guided spatial data generation for complex large-scale indoor map from 3D colorized point cloudsZ. Wang0X. Wang1J. Yang2T. Li3College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaTianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology, China Railway Design Corporation, Tianjin 300251, ChinaWith the popularization of the concept of smart cities and the development of indoor positioning and navigation services, as well as the increasing complexity of building structures with the continuous advancement of urbanization, automatic mapping of large-scale indoor spatial data has become the fundamental work for subsequent real-life 3D applications. Therefore, this paper develops a semantics-guided generation method of indoor spatial data for mapping indoor spaces, where the roles of semantics are investigated for the subdivision and reconstruction of indoor spaces. It consists of the following four parts: (1) Semantic segmentation of 3D indoor scene; (2) Storey segmentation using semantics-enhanced height histogram; (3) Semantics-guided room segmentation based on building physical structures; (4) Room-wise boundary optimization using semantics-aware Recursive Search. Both quantitative and qualitative experiments are conducted on two public benchmark datasets: Stanford Large-Scale 3D Indoor Spaces (S3DIS) dataset and Matterport3D dataset. The results demonstrated that our method is capable of reconstructing complicated large-scale indoor scenes with higher robustness and reliability, outperforming existing state-of-the-art algorithms.https://isprs-annals.copernicus.org/articles/X-G-2025/961/2025/isprs-annals-X-G-2025-961-2025.pdf |
| spellingShingle | Z. Wang X. Wang J. Yang T. Li Semantics-guided spatial data generation for complex large-scale indoor map from 3D colorized point clouds ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Semantics-guided spatial data generation for complex large-scale indoor map from 3D colorized point clouds |
| title_full | Semantics-guided spatial data generation for complex large-scale indoor map from 3D colorized point clouds |
| title_fullStr | Semantics-guided spatial data generation for complex large-scale indoor map from 3D colorized point clouds |
| title_full_unstemmed | Semantics-guided spatial data generation for complex large-scale indoor map from 3D colorized point clouds |
| title_short | Semantics-guided spatial data generation for complex large-scale indoor map from 3D colorized point clouds |
| title_sort | semantics guided spatial data generation for complex large scale indoor map from 3d colorized point clouds |
| url | https://isprs-annals.copernicus.org/articles/X-G-2025/961/2025/isprs-annals-X-G-2025-961-2025.pdf |
| work_keys_str_mv | AT zwang semanticsguidedspatialdatagenerationforcomplexlargescaleindoormapfrom3dcolorizedpointclouds AT xwang semanticsguidedspatialdatagenerationforcomplexlargescaleindoormapfrom3dcolorizedpointclouds AT jyang semanticsguidedspatialdatagenerationforcomplexlargescaleindoormapfrom3dcolorizedpointclouds AT tli semanticsguidedspatialdatagenerationforcomplexlargescaleindoormapfrom3dcolorizedpointclouds |