RECONSTRUCTION OF BUILDINGS FROM AIRBORNE LASER SCANNING DATA

For mapping applications the reconstruction of buildings from the point cloud offers promising prospects for rapid generation of large scale 3D models, e.g., for city modeling. Reconstruction requires, however, knowledge on a variety of parameters that refer both to the point cloud and to the modeled building. For the point cloud the separation of the laser points describing the buildings from the rest of the data is one concern; the other is ensuring that this subset reflects indeed a building and not another object, e.g., vegetation. The generation of a building model from this subset requires then to learn the roof parts from the data, and then convert these elements into an actual building model that complies with topological and geometrical rules. The complexity of this task has led many researchers to use external information, mostly in the form of detailed ground plans to identify the subset of the point cloud and to provide first approximation of the building shape. This information is however not available everywhere and generally cannot be taken for granted. In this paper we present a reconstruction model that automatically detects buildings within the point cloud and reconstruct their shape. For the detection we develop safeguards that reduce the chance of misclassification of a building to a minimum. The reconstruction involves the aggregation of the point set into individual faces, and learning the building shape from these aggregates. We show the effect of imposing geometric constrains on the reconstruction to generate realistic models of the buildings.