Cluster analysis and priority sorting in huge point clouds for building reconstruction

Terrestrial laser scanners produce point clouds with a huge number of points within a very limited surrounding. In built-up areas, many of the man-made objects are dominated by planar surfaces. We introduce a RANSAC based preprocessing technique that transforms the irregular point cloud into a set of locally delimited surface patches in order to reduce the amount of data and to achieve a higher level of abstraction. In a second step, the resulting patches are grouped to large planes while ignoring small and irrelevant structures. The approach is tested with a dataset of a built-up area which is described very well needing only a small number of geometric primitives. The grouping emphasizes man-made structures and could be used as a preclassification.