  Object reconstruction is an important task in many fields of application as
it allows to generate digital representations of our physical world used as
base for analysis, planning, construction, visualization or other aims. A
reconstruction itself normally is based on reliable data (images, 3D point
clouds for example) expressing the object in his complete extent. This data
then has to be compiled and analyzed in order to extract all necessary
geometrical elements, which represent the object and form a digital copy of it.
Traditional strategies are largely based on manual interaction and
interpretation, because with increasing complexity of objects human
understanding is inevitable to achieve acceptable and reliable results. But
human interaction is time consuming and expensive, why many researches has
already been invested to use algorithmic support, what allows to speed up the
process and to reduce manual work load. Presently most of such supporting
algorithms are data-driven and concentate on specific features of the objects,
being accessible to numerical models. By means of these models, which normally
will represent geometrical (flatness, roughness, for example) or physical
features (color, texture), the data is classified and analyzed. This is
successful for objects with low complexity, but gets to its limits with
increasing complexness of objects. Then purely numerical strategies are not
able to sufficiently model the reality. Therefore, the intention of our
approach is to take human cognitive strategy as an example, and to simulate
extraction processes based on available human defined knowledge for the objects
of interest. Such processes will introduce a semantic structure for the objects
and guide the algorithms used to detect and recognize objects, which will yield
a higher effectiveness. Hence, our research proposes an approach using
knowledge to guide the algorithms in 3D point cloud and image processing.
