Bim Reconstruction: Automated Procedural Modeling From Point Cloud Data

Abstract

The reconstruction of Building Information Modeling objects for as-built modeling is currently the subject of ongoing research. A popular method is to extract structure information from point cloud data to create a set of parametric objects. This requires the interpretation of the point cloud data which currently is a manual and labor intensive procedure. Automated processes have to cope with excessive occlusions and clutter in the data sets. To create an as-built BIM, it is vital to reconstruct the building's structure i.e. wall geometry prior to the reconstruction of other objects. In this work, a novel method is presented to automatically reconstruct as-built BIM for generic buildings. We presented an unsupervised method that procedurally models the geometry of the walls based on point cloud data. A bottom-up process is defined where consecutively higher level information is extracted from the point cloud data using pre-trained machine learning models. Prior to the reconstruction, the data is segmented, classified and clustered to retrieve all the available observations of the walls. The resulting geometry is processed by the reconstruction algorithm. First, the necessary information is extracted from the observations for the creation of parametric solid objects. Subsequently, the final walls are created by updating their topology. The method is tested on a variety of scenes and shows promising results to reliably and accurately create as-built models. The accuracy of the generated geometry is similar to the precision of expert modelers. A key advantage is that that the algorithm creates Revit and Rhino native objects which makes the geometry directly applicable to a wide range of applications.This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-2-W17-53-2019 |

Bim on Artificial Intelligence for Decision Support in E-Health

“…Furthermore, just a small change of the data acquisition process or external factors that influence the 3D data can result in a change that turns the elaborately trained model useless. Point cloud data from living environments are characterised by unstructured, scattered and spatiotemporal objects (Chen et al 2019;Bassier et al 2019). Face to such characteristics, the object detection is currently a challenging task despite deep models due to lack of training data.…”