Improving reconstruction of man-made objects from sensor images by machine learning

In this paper we present a new approach for the acquisition and analysis of background knowledge which is used for 3D reconstruction of man-made objects - in this case buildings. Buildings can be easily represented as parameterized graphs from which p-subisomorphic graphs will be computed. P-graphs will be defined and an upper bound complexity estimation of the computation of p-subisomorphims will be given. In order to reduce search space we will discuss several pruning mechanisms. Background knowledge requires a classification in order to receive a probability distribution which will serve as a priori knowledge for 3D building reconstruction. Therefore, we will apply an alternative view of nearest-neighbor classification to measured knowledge in order to learn based on a complete seed and a noise model a distribution of this knowledge. An application of an extensive scene consisting of 1846 building cluster which are represented as p-graphs in order to estimate a probability distribution of corner nodes demonstrates the effectivness of our approach. An evaluation using the information coding theory determines the information gain which is provided by the estimated distribution in comparison with no available a priori knowledge.