Automation in Architectural Photogrammetry

Abstract

Architectural photogrammetry has been practised for more than a century for the documentation of cultural heritage. Nowadays, the emphasis is on the construction of computer models for virtual reality applications. Since the introduction of the computer, and later the digital camera, research in photogrammetry aims at automation. This thesis reports on research on automation in architectural photogrammetry for efficient reconstruction of detailed building models from one or more, possibly widely separated, digital close-range images. This research lies on the fringes of photogrammetry and computer vision. It treats topics frequently studied in computer vision in a photogrammetric way and offers new solutions. Examples cover interior orientation and reconstruction from a single mage, vanishing point detection, and the wide-baseline stereo problem. A semi-automatic approach is chosen that exploits knowledge of the object shape, such as planarity of facades, rectangular and repeating structures in the building, and shape symmetries. Automatically or manually extracted straight image line features are the main observations in the line-photogrammetric approaches presented in this thesis. Furthermore, the methods developed are characterised by the use of robust direct solutions for approximate value computation, followed by least-squares adjustment in which the knowledge of the shape of the building is processed together with the image line observations. This integral adjustment provides optimal estimates for the object model parameters and facilitates quality assessment.