A Mean Shift Based Competition Algorithm for Robust Extraction of Roof Plane from Airborne Laser Scanning Data
نویسندگان
چکیده
Laser scanning data has been an important raw source for building model extraction. However, the automatic reconstruction of building model is still a problem. Roof plane Extraction from point cloud is a key step to building reconstruction, which can be easily affected by the noise existed in laser scanning data. It is often difficult to determine which plane a point should belong to when the points are near the boundary of a surface. In this paper, a mean shift based roof plane extraction method is proposed to solve this problem. Normal angle of every points are used as color in LUV space for extraction belong to different direction. And the z change is also involved to separate parallel planes. The vector composed of point angle and z value becomes important information to determine each plane. By apply mean shift segmentation algorithm to these vectors, major roof planes can be extracted. Because mean shift algorithm has the competition mechanism under the theory, it can properly select the best plane which the points belong to. Tests results with point cloud of complex roofs are given to prove the method proposed.
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