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Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization
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DOI：10.1016/j.cad.2012.10.014.png)
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This paper presents an unsupervised algorithm for co-segmentation of a set of 3D shapes of the same family. Taking the over-segmentation results as input, our approach clusters the primitive patches to generate an initial guess. Then, it iteratively builds a statistical model to describe each cluster of parts from the previous estimation, and employs the multi-label optimization to improve the co-segmentation results. In contrast to the existing one-shot algorithms, our method is superior in that it can improve the co-segmentation results automatically. The experimental results on the Princeton Segmentation Benchmark demonstrate that our approach is able to co-segment 3D shapes with significant variability and achieves comparable performance to the existing supervised algorithms and better performance than the unsupervised ones. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Co-segmentation
Unsupervised algorithm
Shape correspondence
Multi-label optimization
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3.1
论文数:
3.1K
被引数:
6.4K
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