JOINT SIMULTANEOUS RECONSTRUCTION OF REGULARIZED BUILDING SUPERSTRUCTURES FROM LOW-DENSITY LIDAR DATA USING ICP

Abstract. There are many applications for 3D city models, e.g., in visualizations, analysis, and simulations; each one requiring a certain level of detail to be effective. The overall trend goes towards including various kinds of anthropogenic and natural objects therein with ever increasing geometric and semantic details. A few years back, the featured 3D building models had only coarse roof geometry. But nowadays, they are expected to include detailed roof superstructures like dormers and chimneys. Several methods have been proposed for the automatic reconstruction of 3D building models from airborne based point clouds. However, they are usually unable to reliably recognize and reconstruct small roof superstructures as these objects are often represented by only few point measurements, especially in low-density point clouds. In this paper, we propose a recognition and reconstruction approach that overcomes this problem by identifying and simultaneously reconstructing regularized superstructures of similar shape. For this purpose, candidate areas for superstructures are detected by taking into account virtual sub-surface points that are assumed to lie on the main roof faces below the measured points. The areas with similar superstructures are detected, extracted, grouped together, and registered to one another with the Iterative Closest Point (ICP) algorithm. As an outcome, the joint point density of each detected group is increased, which helps to recognize the shape of the superstructure more reliably and in more detail. Finally, all instances of each group of superstructures are modeled at once and transformed back to their original position. Because superstructures are reconstructed in groups, symmetries, alignments, and regularities can be enforced in a straight-forward way. The validity of the approach is presented on a number of example buildings from the Vaihingen test data set.