Hierarchical face clustering on polygonal surfaces
I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
Variational shape approximation
ACM SIGGRAPH 2004 Papers
Bayesian hierarchical clustering
ICML '05 Proceedings of the 22nd international conference on Machine learning
Hierarchical mesh segmentation based on fitting primitives
The Visual Computer: International Journal of Computer Graphics
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Nonparametric density estimation with adaptive, anisotropic kernels for human motion tracking
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Least squares quantization in PCM
IEEE Transactions on Information Theory
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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.