Browsing by Author "Krispel, Ulrich"

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Generative Methods for Data Completion in Shape Driven Systems
    (2018) Krispel, Ulrich
    In many application domains, such as building planning, construction, or documentation, it is of high importance to acquire a digital representation of the shape of real world objects, e.g. for visualization or documentation purposes. Such objects are often part of a class or domain of similarly structured objects; and often complex objects, such as houses, are composed by simpler objects, such as walls, doors and windows. Especially man-made objects exhibit such structure, mostly due to manufacturability and design reasons. A rich digital representation of a complex object consists not only of its shape, but also its structure, i.e. the composition hierarchy of simpler objects. A more general way to represent such a composition hierarchy is a generative model, that generates the structure upon evaluation; a parametric generative model can generate a whole class of similarly structured objects. In this thesis, I review shape-based methods for generative creation of models, and present a novel system for generative forward modeling based on shape grammars. Furthermore, I present two methods for solving the inverse problem: acquiring a rich digital representation of real-world objects from measurements and utilizing a generative model of prior domain knowledge. Using this prior knowledge, it is now possible to complete missing features, or reduce measurement errors. The first method parses the hierarchical structure of a building faƧade, given an ortho photo and a grammar that describes architectural constraints. The second method yields a hypothesis of electrical wiring inside walls, given optical measurements (point clouds and photographs), and a grammar that describes the technical standards.