Assessment of the quality of as-is building information models generated from point clouds using deviation analysis

Three dimensional (3D) imaging sensors, such as laser scanners, are being used to create building information models (BIMs) of the as-is conditions of buildings and other facilities. Quality assurance (QA) needs to be conducted to ensure that the models accurately depict the as-is conditions. We propose a new approach for QA that analyzes patterns in the raw 3D data and compares the 3D data with the as-is BIM geometry to identify potential errors in the model. This "deviation analysis" approach to QA enables users to analyze the regions with significant differences between the 3D data and the reconstructed model or between the 3D data of individual laser scans. This method can help identify the sources of errors and does not require additional physical access to the facility. To show the approach's potential effectiveness, we conducted case studies of several professionally conducted as-is BIM projects. We compared the deviation analysis method to an alternative method - the physical measurement approach - in terms of errors detected and coverage. We also conducted a survey and evaluation of commercial software with relevant capabilities and identified technology gaps that need to be addressed to fully exploit the deviation analysis approach.