The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIX-B2-2026
https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-1221-2026
https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-1221-2026
23 Jul 2026
 | 23 Jul 2026

Graph-based topology retrieval and constructive solid geometry for structural BIM refinement

Muataz S. A. Albadri, Patricia González-Cabaleiro, Antonio Fernández, and Lucía Díaz-Vilariño

Keywords: Scan-to-BIM, as-built BIM, IFC, CSG, connectivity graph, automatic modelling

Abstract. As-built Building Information Models (BIMs) are crucial for building digitalisation, structural analysis, and life cycle management. Despite recent advances, automated reconstruction of structural elements from point clouds remains a challenging task, particularly in ensuring geometric accuracy and topological consistency within a storey and across consecutive storeys. This paper proposes an automated method for refining topological inconsistency between columns, beams, and slabs, ensuring consistent as-built BIMs. The method places Constructive Solid Geometry (CSG) at the core of refinement process, driven by fundamental structural principles. The method starts by creating solid rectangular prisms from labelled point clouds. Beams are then aligned both vertically and horizontally within each storey. Columns are vertically aligned across consecutive storeys. Topology relationships between the elements are retrieved and encoded in graphs. These graphs, together with a set of Boolean operations, are used to resolve gaps and trim overlaps between the connected elements. The refined elements are represented in accordance with the IFC standards. The proposed method was validated on two multi-storey case studies representing frame and flat-slab building structures. Both qualitative and quantitative evaluations confirmed the effectiveness of the approach, achieving significant geometric accuracy and topological consistency. In addition, the method exhibits efficient runtime performance, indicating its promise for scalable Scan-to-BIM automation.

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