Abstract
In this paper, a new and robust approach to mesh segmentation is presented. There are various algorithms which deliver satisfying results on clean 3D models. However, many reverse-engineering applications in computer vision such as 3D reconstruction produce extremely noisy or even incomplete data. The presented segmentation algorithm copes with this challenge by a robust semi-global clustering scheme and a cost-function that is based on a probabilistic model. Vision based reconstruction methods are able to generate colored meshes and it is shown, how the vertex color can be used as a supportive feature. A probabilistic framework allows the algorithm to be easily extended by other user defined features. The segmentation scheme is a local iterative optimization embedded in a hierarchical clustering technique. The presented method has been successfully tested on various real world examples.
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Wekel, T., Hellwich, O. (2011). A Robust Approach to Multi-feature Based Mesh Segmentation Using Adaptive Density Estimation. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_30
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DOI: https://doi.org/10.1007/978-3-642-23672-3_30
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