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Geometric segmentation and object recognition in unordered and incomplete point cloud

: Ahn, S.J.; Effenberger, I.; Roth-Koch, S.; Westkämper, E.

Michaelis, B. ; Deutsche Arbeitsgemeinschaft für Mustererkennung -DAGM-:
Pattern recognition : Magdeburg, Germany, September 10 - 12, 2003. Proceedings, 25th DAGM Symposium
Berlin: Springer, 2003 (Lecture Notes in Computer Science 2781)
ISBN: 3-540-40861-4
ISSN: 0302-9743
Deutsche Arbeitsgemeinschaft für Mustererkennung (Symposium) <25, 2003, Magdeburg>
Fraunhofer IPA ()
point cloud; Punktwolke; pattern recognition; Bilderkennung

In applications of optical 3D-measurement techniques segmentation and outlier elimination in point clouds is a tedious and time-consuming task. In this paper, we present a very robust and efficient procedure of segmentation, outlier elimination, and model fitting in point clouds. For an accurate reliable estimation of the model parameters, we apply orthogonal distance fitting (ODF) algorithms theat minimize the square sum of the geometric error distances. The model parameters are grouped and simultaneously estimated in terms of form, position, and rotation parameters, hence providing a very advantageous algorithmic feature for segmentation and object recognition.