Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization
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 cluste...
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| Vydáno v: | Computer aided design Ročník 45; číslo 2; s. 312 - 320 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.02.2013
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| Témata: | |
| ISSN: | 0010-4485, 1879-2685 |
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| Abstract | 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.
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| AbstractList | 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 aone-shota 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. 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. [Display omitted] |
| Author | Luo, Jun Meng, Min He, Ying Xia, Jiazhi |
| Author_xml | – sequence: 1 givenname: Min surname: Meng fullname: Meng, Min email: mengmin@ntu.edu.sg organization: School of Computer Engineering, Nanyang Technological University, Singapore – sequence: 2 givenname: Jiazhi surname: Xia fullname: Xia, Jiazhi email: jzxia@ntu.edu.sg organization: BeingThere Center, Institute for Media Innovation, Nanyang Technological University, Singapore – sequence: 3 givenname: Jun surname: Luo fullname: Luo, Jun email: junluo@ntu.edu.sg organization: School of Computer Engineering, Nanyang Technological University, Singapore – sequence: 4 givenname: Ying surname: He fullname: He, Ying email: yhe@ntu.edu.sg organization: School of Computer Engineering, Nanyang Technological University, Singapore |
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| Keywords | Co-segmentation Unsupervised algorithm Multi-label optimization Shape correspondence |
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| Snippet | 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... |
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| SubjectTerms | Algorithms Benchmarking Clusters Co-segmentation Computer aided design Construction Mathematical models Multi-label optimization Optimization Shape correspondence Three dimensional Unsupervised algorithm |
| Title | Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization |
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