Corpus ID: 21161163

Deep Spectral Descriptors: Learning the point-wise correspondence metric via Siamese deep neural networks

@article{Sun2017DeepSD,
  title={Deep Spectral Descriptors: Learning the point-wise correspondence metric via Siamese deep neural networks},
  author={Zhiyu Sun and Yusen He and Andrey Gritsenko and Amaury Lendasse and Stephen Seung-Yeob Baek},
  journal={ArXiv},
  year={2017},
  volume={abs/1710.06368}
}
  • Zhiyu Sun, Yusen He, +2 authors Stephen Seung-Yeob Baek
  • Published 2017
  • Computer Science
  • ArXiv
  • A robust and informative local shape descriptor plays an important role in mesh registration. [...] Key Method We design and train a Siamese deep neural network to find such an embedding, where the embedded descriptors are promoted to rearrange based on the geometric similarity. We found our approach can significantly enhance the performance of the conventional spectral descriptors for the non-isometric registration tasks, and outperforms recent state-of-the-art method reported in literature.Expand Abstract

    Citations

    Publications citing this paper.
    SHOWING 1-9 OF 9 CITATIONS

    EdgeNet: Deep metric learning for 3D shapes

    Modeling and Optimizing Building HVAC Energy Systems Using Deep Neural Networks

    • Jiahao Deng, Haoran Wang
    • Environmental Science
    • 2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)
    • 2018
    VIEW 1 EXCERPT
    CITES BACKGROUND

    Risk Assessment of Groundwater Depletion Induced Land Subsidence: A Case Study in Taiyuan Basin, China

    VIEW 1 EXCERPT
    CITES BACKGROUND

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 49 REFERENCES

    Discriminative Learning of Deep Convolutional Feature Point Descriptors

    Geodesic Convolutional Neural Networks on Riemannian Manifolds

    Scale-invariant heat kernel signatures for non-rigid shape recognition

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Dense Human Body Correspondences Using Convolutional Networks