Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization

Authors:
Min Meng;Jiazhi Xia;Jun Luo;Ying He
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;BeingThere Center, Institute for Media Innovation, Nanyang Technological University, Singapore and School of Information Science and Engineering, Central South University, China;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
Computer-Aided Design
Year:
2013

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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.