Abstract
A robust registration of 3D-point data and a triangle mesh of the corresponding 3D-structure is presented, where the acquired 3D-point data may be noisy, may include outliers and may have wrong scale. Furthermore, in this approach it is not required to have a good initial match so the 3D-point cloud and the according triangle mesh may be loosely positioned in space. An additional advantage is that no correspondences have to exist between the 3D-points and the triangle mesh. The problem is solved utilizing a robust cost function in combination with an evolutionary global optimizer as shown in synthetic and real data experiments.
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© 2006 Springer-Verlag Berlin Heidelberg
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Urfalıoḡlu, O., Mikulastik, P., Stegmann, I. (2006). Scale Invariant Robust Registration of 3D-Point Data and a Triangle Mesh by Global Optimization. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_96
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DOI: https://doi.org/10.1007/11864349_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
Online ISBN: 978-3-540-44632-3
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