Deviation analysis method for the assessment of the quality of the as-is Building Information Models generated from point cloud data

Generating three-dimensional (3D) as-is Building Information Models (BIMs), representative of the existing conditions of buildings, from point cloud data collected by laser scanners is becoming common practice. However, generation of such models currently is mostly performed manually, and errors can be introduced during data collection, pre-processing, and modeling. This paper presents a method for assessing the quality of as-is BIMs generated from point cloud data by analyzing the patterns of geometric deviations between the model and the point cloud data. The fundamental assumption is that the point cloud and the as-is BIM generated from the point cloud should corroborate in the depiction of the components and their spatial attributes. Major geometric deviations between as-is models and point clouds can indicate potential errors introduced during data collection, processing and/or model generation. The research described in this paper provides a taxonomy for patterns of deviations and sources of errors and demonstrates that it is possible to identify the source, magnitude, and nature of errors by analyzing the deviation patterns. The method is validated through a comparison with the currently adopted physical measurement method in a case study. The results show that the deviation analysis method is capable of identifying almost six times more errors with more than 40% time savings compared to the physical measurement method.