SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation

@article{Yi2016SyncSpecCNNSS,
  title={SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation},
  author={L. Yi and Hao Su and Xingwen Guo and Leonidas J. Guibas},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={6584-6592}
}
  • L. YiHao Su L. Guibas
  • Published 2 December 2016
  • Computer Science
  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
In this paper, we study the problem of semantic annotation on 3D models that are represented as shape graphs. A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN… 

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