A Step towards Semantic-based Building Reconstruction Using Markov-random-fields

In this paper we describe a new concept for the reconstruction of buildings. In contrast to most of the published approaches, we link the reconstruction process with the building interpretation. With this linkage we want to enhance the reconstruction result and to yield semantic information about the buildings. We introduce building models based on their topology. We also may use data from different sensor types. The analysis is done locally using statistical building information for the interpretation in a Markov-Random-Field and using e. g. geometric or radiometric “appearance” models for the reconstruction. A real data example from laserscanner observations demonstrates the approach.