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SCIENCE CHINA Information Sciences, Volume 61, Issue 5: 054101(2018) https://doi.org/10.1007/s11432-017-9331-9

3D shape co-segmentation via sparse and low rank representations

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  • ReceivedOct 30, 2017
  • AcceptedJan 17, 2018
  • PublishedApr 10, 2018

Abstract

In this paper, we propose a 3D shape co-segmentation method, which divides 3D shapes in the same category into consistent feature representations. We involve sparse and low-rank constraints to obtain compact feature representations among the 3D shapes. After pre-segmentation and feature extraction processes, we convert the co-segmentation problem into feature clustering issues. With the sparse and low-rank constraints, the initial geometry features are mapped into a compact coefficient space. Then, we gather the coefficients and weight them by a confidence weighting procedure. Finally, we apply fuzzy cuts method for optimization and achieve the final shape co-segmentation results. Experimental results on two public benchmarks demonstrate that our approach is robust for various 3D meshes, and outperforms other state-of-the-art approaches.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61532003, 61421003).


Supplement

Videos and other supplemental documents.


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