In construction, dimensional quality is critical but is very difficult to achieve, especially with built-in-place elements. As a result, dimensional Quality Assessment / Quality Control (QA/QC) must systematically be conducted, which often delays the value-adding work. Current methods for dimensional QA/QC are labor intensive, time consuming and therefore expensive. Comprehensive dimensional QA/QC approaches are thus often discarded for strategic ones, which may provide misleading dimensional QA/QC results, and result in future rework or failures. In the research presented here, the authors take advantage of new technologies available to the Architectural Engineering Construction and Facility Management industry -- 3D Computer-Aided Design (CAD) engines, 3D positioning technologies and 3D laser scanners -- to develop a method for automated retrieval of 3D CAD model objects in 3D laser scanner range images. This approach for automated CAD object retrieval allows for the automated and accurate segmentation of the as-built cloud corresponding to each project 3D CAD object, and it is robust with respect to occlusions. The quality of the output data is such that it is possible to use it to perform automated defect detection for dimensional QA/QC. In this paper, the authors first present the developed approach and demonstrate its efficiency through a simple experiment. Then, the authors discuss in more detail how the retrieval output data can be used to support automated dimensional QA/QC.