Surface reconstruction from LiDAR data with extended snake theory
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
An accumulating interpreter for cognitive vision production systems
Pattern Recognition and Image Analysis
An accumulating interpreter for cognitive vision production systems
Pattern Recognition and Image Analysis
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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 builtup 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.