Automated BIM Reconstruction from Point Clouds Using IFC Standards in Indoor Environment

Point cloud data acquired through LiDAR technology enables the rapid reconstruction of complex indoor building structures. However, due to the discrete nature of point clouds, they fail to accurately represent the geometric dimensions of building components and cannot be directly applied to digital model construction. Conventionally, this limitation necessitates manual modeling in specialized BIM software to integrate both geometric and semantic information of building structures, which is labor-intensive and time-consuming. To address this, we propose an automated method for geometric feature extraction and BIM reconstruction, enabling more efficient and accurate modeling processes. By segmenting building components and extracting their geometric features, the method automates the construction of building structural entities based on the IFC (Industry Foundation Class) standard, which is an open and vendor-neutral modeling standard widely used in the BIM domain.

Specifically, the approach starts by filtering ceilings and floors using histograms of height values, as they geometrically represent planar structures and can be represented by footprints, which commonly are constructed from closed contours formed by projecting wall segments onto horizontal planes. To ensure accurate footprint representation, non-wall objects, such as furniture, are first excluded from the scene. For this purpose, a series of viewpoints are used to simulate camera positions and generate image sequences. Coupled with these image sequences, a pretrained large-scale language-image model, YOLO-World, is applied to identify the bounding boxes of the furniture, while the SAM2 model is used to segment individual entities. The segmented pixels are then back-projected and aggregated in 3D space to isolate and exclude non-building objects. Once the walls are identified, the point clouds are processed using a region-growing and merging algorithm to extract multiple facades, which are projected onto horizontal planes to generate line segments. Based on these line segments and the scene’s bounding box, the horizontal plane is divided into cell partitions, and an energy optimization-based graph-cut algorithm is applied to identify the optimal cell set, with the resulting closed contours representing the footprints. These footprints are then extruded along the height direction into 3D geometries to generate Ifcwall, Ifcceiling, and Ifcfloor objects through the Ifcopenshell library, producing a complete and standardized IFC model.

This method was validated in complex indoor environments with furniture such as tables, chairs, and sofas, demonstrating high precision in reconstructing fundamental building components like walls, floors, and ceilings. By providing an automated and efficient solution for simple indoor structure reconstruction, the approach lays the groundwork for modeling more intricate scenarios and facilitates the development of intelligent, sustainable digital twin models to support comprehensive lifecycle building management.