Smart City Gnosys
Smart city article details
| Title | Deep Learning Semantic Segmentation-Based Scan-To-Bim For Indoor Point Clouds |
|---|---|
| ID_Doc | 17915 |
| Authors | Mahmoud M.; Adham M.; Li Y.; Chen W. |
| Year | 2025 |
| Published | 2025 15th International Conference on Electrical Engineering, ICEENG 2025 |
| DOI | http://dx.doi.org/10.1109/ICEENG64546.2025.11031284 |
| Abstract | Building information modeling (BIM) is essential for effectively managing reconstructed buildings throughout their operational and maintenance stages, aiding in the progress of smart city projects. However, conventional scan-to-BIM techniques often depend on manual or semi-automated processes and do not fully exploit the rich semantic details of point clouds. As a result, these methods encounter difficulties in accurately modeling large, intricate indoor environments, leading to inefficiencies in BIM creation. To overcome these challenges, this paper presents a novel scan-to-BIM pipeline that integrates deep learning approaches and laser scanner point clouds. The proposed method consists of several key stages, such as preprocessing of scanned data, semantic segmentation through an improved deep learning model, clustering of room density, detection of room clusters and segments, extraction of segment features, identification of doors and windows, and the automated generation of BIM models. These processes work together to automatically create precise and detailed 3D models of building structures. The pipeline is capable of handling large datasets, accommodating both Manhattan and non-Manhattan geometries, and employing a parametric BIM algorithm in Revit software for automated reconstruction. The pipeline has been validated with point cloud data of various indoor layouts, demonstrating outstanding performance in reconstructing indoor elements, with precision and recall values of 97.60% and 96.70%, respectively. The resulting BIM models show excellent completeness and geometric accuracy, surpassing traditional methods in both efficiency and quality. © 2025 IEEE. |
| Author Keywords | BIM; deep learning; point cloud data; scan-to-BIM; semantic segmentation |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
11176  | 0.972 | Mahmoud M.; Chen W.; Yang Y.; Li Y. | Automated Bim Generation For Large-Scale Indoor Complex Environments Based On Deep Learning | Automation in Construction, 162 (2024) |
| 46412 | 0.945 | Wang H.; Chen T.; Li D.; Liu J.; Wu Z.; Wu Y. | Reverse Modeling Of Indoor Scenes: From Point Cloud Scanning To Bim Reconstruction | Lecture Notes in Civil Engineering, 599 LNCE (2025) |
| 17515 | 0.911 | Liu Y.; Huang H.; Gao G.; Ke Z.; Li S.; Gu M. | Dataset And Benchmark For As-Built Bim Reconstruction From Real-World Point Cloud | Automation in Construction, 173 (2025) |
| 55165 | 0.9 | Zhao S.; Yang Y. | The Construction And Application Of Residential Building Information Model Based On Deep Learning Algorithms | Scalable Computing, 25, 5 (2024) |
| 1779 | 0.888 | Zuo Z.; Li Y. | A Framework For Reconstructing Building Parametric Models With Hierarchical Relationships From Point Clouds | International Journal of Applied Earth Observation and Geoinformation, 119 (2023) |
| 48214 | 0.872 | Kolbe T.H.; Donaubauer A. | Semantic 3D City Modeling And Bim | Urban Book Series (2021) |
| 13030 | 0.862 | Zhang Y.; Wang T.; Lin X.; Zhao Z.; Wang X. | Building Extraction From Lidar Point Clouds Based On Revised Randla-Net | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 48, 1 (2024) |
| 22660 | 0.854 | Mao Y.; Chen K.; Zhao L.; Chen W.; Tang D.; Liu W.; Wang Z.; Diao W.; Sun X.; Fu K. | Elevation Estimation-Driven Building 3-D Reconstruction From Single-View Remote Sensing Imagery | IEEE Transactions on Geoscience and Remote Sensing, 61 (2023) |
| 48319 | 0.853 | Cheng H.; Zi W.; Peng S.; Chen H. | Semi-Supervised Learning Based 3D Mesh Building Facade Extraction And Semantic Segmentation Method; [基于半监督学习的三维 Mesh 建筑物立面提取与语义分割方法] | Journal of Zhengzhou University - Natural Science, 55, 4 (2023) |
| 40412 | 0.851 | Li S.; Yan F.; Ma K.; Hu Q.; Wang F.; Liu W. | Optimal Feature-Guided Position-Shape Dual Optimization For Building Point Cloud Facade Detail Enhancement | Remote Sensing, 16, 22 (2024) |