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.
Surface flatness assessment is required for controlling the quality of various products, such as building and mechanical
components. During such assessments, inspectors collect data capturing surface shape, and use it to identify flatness
defects, which are surface parts deviating from a reference plane by more than the tolerance. Laser scanners can deliver
accurate and dense 3D point clouds capturing detailed surface shape for flatness defect detection in minutes. However,
few studies explore algorithms for detecting surface flatness defects from dense point clouds, and provide quantitative
analysis of defect detection performance. This paper presents three surface-flatness-defect detection algorithms and our
experimental investigations for characterizing their performances. We created a test bed, which is composed of several
flat boards with defects of various sizes on them, and tested two scanners and three algorithms using it. The results are
reported in the form of a set of performance maps indicating under which conditions (using which scanner, scanning
distance, selected defect detection algorithm, and angular resolution of the scanner, etc.), what types of defects are
detected. Our analysis shows that scanning distance and angular resolution substantially influence the detection accuracy.
Comparative analyses of scanners and defect detection algorithms are also presented.