Automated digital modeling of existing buildings: A review of visual object recognition methods

Digital building representations enable and promote new forms of simulation, automation, and information sharing. However, creating and maintaining these representations is prohibitively expensive. In an effort to make the adoption of this technology easier, researchers have been automating the digital modeling of existing buildings by applying reality capture devices and computer vision algorithms. This article is a summary of the efforts of the past ten years, with a particular focus on object recognition methods. We rectify three limitations of existing review articles by describing the general structure and variations of object recognition systems and performing an extensive and quantitative comparative performance evaluation. The coverage of building component classes (i.e. semantic coverage) and recognition performances are reported in-depth and framed using a building taxonomy. Research programs demonstrate sparse semantic coverage with a clear bias towards recognizing floor, wall, ceiling, door, and window classes. Comprehensive semantic coverage of building infrastructure will require a radical scaling and diversification of efforts.