dc.contributor.authorMeng, Min
dc.contributor.authorXia, Jiazhi
dc.contributor.authorLuo, Jun
dc.contributor.authorHe, Ying
dc.identifier.citationMeng, M., Xia, J., Luo, J., & He, Y. (2013). Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization. Computer-Aided Design, 45(2), 312-320.en_US
dc.description.abstractThis 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.en_US
dc.relation.ispartofseriesComputer-aided designen_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titleUnsupervised co-segmentation for 3D shapes using iterative multi-label optimizationen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US

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