Point clouds segmentation as base for as-built BIM creation

23 citations as recorded by crossref.

1. Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes J. Ponciano et al. https://doi.org/10.3390/ijgi8100442
2. Domain‐adaptive self‐supervised learning for corrosion detection and 3D building information model mapping in steel tunnels S. Maharjan et al. https://doi.org/10.1111/mice.70077
3. Quantitative Evaluation of CHT and GHT for Column Detection under Different Conditions of Data Quality M. Bueno et al. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000678
4. Semi-automatic 3D frame modelling of wooden trusses using indoor point clouds R. Otero et al. https://doi.org/10.1016/j.istruc.2022.11.122
5. A Preliminary Study of 3D Vernacular Documentation for Conservation and Evaluation: A Case Study in Keraton Kasepuhan Cirebon S. Trisyanti et al. https://doi.org/10.3390/buildings13020546
6. Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry A. Murtiyoso et al. https://doi.org/10.3390/s22030966
7. Automated CAD-to-BIM generation of restroom sanitary plumbing system M. Shin et al. https://doi.org/10.1093/jcde/qwae021
8. Towards Real-Time 3D Editable Model Generation for Existing Indoor Building Environments on a Tablet A. Arnaud et al. https://doi.org/10.3389/frvir.2022.782564
9. Smartphone LiDAR Data: A Case Study for Numerisation of Indoor Buildings in Railway Stations O. Catharia et al. https://doi.org/10.3390/s23041967
10. Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey S. Pokuciński & D. Mrozek https://doi.org/10.3390/app14103974
11. Evaluation of LiDAR-Derived Features Relevance and Training Data Minimization for 3D Point Cloud Classification S. Morsy & A. Shaker https://doi.org/10.3390/rs14235934
12. Developing final as-built BIM model management system for owners during project closeout: A case study Y. Lin et al. https://doi.org/10.1016/j.aei.2018.04.001
13. Classification of 3D Digital Heritage E. Grilli & F. Remondino https://doi.org/10.3390/rs11070847
14. Indoor 3D Reconstruction of Buildings via Azure Kinect RGB-D Camera C. Delasse et al. https://doi.org/10.3390/s22239222
15. Automated Reconstruction of Existing Building Interior Scene BIMs Using a Feature-Enhanced Point Transformer and an Octree J. Chen et al. https://doi.org/10.3390/app132413239
16. Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM M. Bassier et al. https://doi.org/10.36680/j.itcon.2020.011
17. Classification of sensor independent point cloud data of building objects using random forests M. Bassier et al. https://doi.org/10.1016/j.jobe.2018.04.027
18. Automated data-driven method for creating digital building models from dense point clouds and images through semantic segmentation and parametric model fitting M. Mehranfar et al. https://doi.org/10.1016/j.aei.2024.102643
19. Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements A. Murtiyoso & P. Grussenmeyer https://doi.org/10.3390/s20082161
20. From Point Cloud Data to Structural Analysis Through a Geometrical hBIM-Oriented Model R. Rolin et al. https://doi.org/10.1145/3242901
21. Heritage Smart City Mapping, Planning and Land Administration (Hestya) D. Suwardhi et al. https://doi.org/10.3390/ijgi11020107
22. A review on HBIM modelling process from 3D point clouds by applying artificial intelligence algorithms in cultural heritage H. Şentürk & C. Şimşek https://doi.org/10.2339/politeknik.1503631
23. A Semi-Automatic Annotation Methodology for Photogrammetric Images of Heritage Buildings E. Pellis et al. https://doi.org/10.1080/15583058.2025.2586029