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The Visual Computer

, Volume 35, Issue 6–8, pp 909–920 | Cite as

Image-driven unsupervised 3D model co-segmentation

  • Juncheng Liu
  • Paul L. Rosin
  • Xianfang Sun
  • Jianguo Xiao
  • Zhouhui LianEmail author
Original Article
  • 155 Downloads

Abstract

Segmentation of 3D models is a fundamental task in computer graphics and vision. Geometric methods usually lead to non-semantic and fragmentary segmentations. Learning techniques require a large amount of labeled training data. In this paper, we explore the feasibility of 3D model segmentation by taking advantage of the huge number of easy-to-obtain 2D realistic images available on the Internet. The regional color exhibited in images provides information that is valuable for segmentation. To transfer the segmentations, we first filter out inappropriate images with several criteria. The views of these images are estimated by our proposed texture-invariant view estimation Siamese network. The training samples are generated by rendering-based synthesis without laborious labeling. Subsequently, we transfer and merge the segmentations produced by each individual image by applying registration and a graph-based aggregation strategy. The final result is obtained by combining all segmentations within the 3D model set. Our qualitative and quantitative experimental results on several model categories validate effectiveness of our proposed method.

Keywords

3D segmentation Image-driven View estimation 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos.: 61672043 and 61672056), National Key Research and Development Program of China (2017YFB1002601) and Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology). The support provided by China Scholarship Council (CSC) during a visit of Juncheng Liu to Cardiff University is acknowledged.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Juncheng Liu
    • 1
    • 2
  • Paul L. Rosin
    • 2
  • Xianfang Sun
    • 2
  • Jianguo Xiao
    • 1
  • Zhouhui Lian
    • 1
    Email author
  1. 1.Institute of Computer Science and TechnologyPeking UniversityBeijingChina
  2. 2.School of Computer Science and InformaticsCardiff UniversityCardiffUK

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