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Abstract |
We present an investigation on the use of Tensor Voting for categorizing LIDAR data into outliers, line elements (e.g. high-voltage power lines), surface patches (e.g. roofs) and volumetric elements (e.g. vegetation).
The Reconstruction of man-made objects is a main task of photogrammetry. With the increasing quality and availability of LIDAR sensors, range data is becoming more and more important. With LIDAR sensors it is possible to quickly aquire huge amounts of data. But in contrast to classical systems, where the measurement points are chosen by an operator, the data points do not explicitly correspond to meaningful points of the object, i.e. edges, corners, junctions. To extract these features it is necessary to segment the data into homogeneous regions wich can be processed afterwards.
Our approach consists of a two step segmentation. The first one uses the Tensor Voting algorithm. It encodes every data point as a particle which sends out a vector field. This can be used to categorize the pointness, edgeness and surfaceness of the data points. After the categorization of the given LIDAR data points also the regions between the data points are rated. Meaningful regions like edges and junctions, given by the inherent structure of the data, are extracted.
In a second step the so labeled points are merged due to a similarity constraint. This similarity constraint is based on a minimum description length principle, encoding and comparing different geometrical models.
The output of this segmentation consists of non overlapping geometric objects in three dimensional space.
The approach is evaluated with some examples of Lidar data. |
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