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| Title | Dataset And Benchmark For As-Built Bim Reconstruction From Real-World Point Cloud |
|---|---|
| ID_Doc | 17515 |
| Authors | Liu Y.; Huang H.; Gao G.; Ke Z.; Li S.; Gu M. |
| Year | 2025 |
| Published | Automation in Construction, 173 |
| DOI | http://dx.doi.org/10.1016/j.autcon.2025.106096 |
| Abstract | As-built BIM reconstruction plays a significant role in urban renewal and building digitization but currently faces challenges of low efficiency. Scan-to-BIM aims to improve reconstruction efficiency but lacks domain-specific, large-scale datasets and accurate, multi-dimensional benchmark metrics. These deficiencies further impede the evaluation and training of scan-to-BIM methods. To address these challenges, this paper proposes BIMNet, an IFC-based large-scale point cloud to BIM dataset, and a set of metrics that reflect the quality and issues of reconstructed models from both geometric and topological perspectives. Experiments demonstrate that BIMNet enhances the evaluation and training of scan-to-BIM methods during the critical processes of reconstruction and segmentation. This research contributes to the data foundation and metric system for deep-learning based scan-to-BIM methods. In the future, BIMNet will not only facilitate the development of scan-to-BIM but also contribute to the advancement of smart cities and AI-driven technologies beyond scan-to-BIM. © 2025 Elsevier B.V. |
| Author Keywords | As-built environment; Building information modeling (BIM); Dataset and benchmark; Industrial foundation class (IFC); Point cloud semantic segmentation; Scan-to-BIM |
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