TR2021-121

Graph Signal Processing for Geometric Data and Beyond: Theory and Applications


    •  Hu, W., Pang, J., Liu, X., Tian, D., Lin, C.-W., Vetro, A., "Graph Signal Processing for Geometric Data and Beyond: Theory and Applications", IEEE Transactions on Multimedia, October 2021.
      BibTeX TR2021-121 PDF
      • @article{Hu2021oct,
      • author = {Hu, Wei and Pang, Jiahao and Liu, Xianming and Tian, Dong and Lin, Chia-Wen and Vetro, Anthony},
      • title = {Graph Signal Processing for Geometric Data and Beyond: Theory and Applications},
      • journal = {IEEE Transactions on Multimedia},
      • year = 2021,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2021-121}
      • }
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  • Research Area:

    Computer Vision

Abstract:

Geometric data acquired from real-world scenes, e.g., 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc. Due to irregular sampling patterns of most geometric data, traditional image/video processing methodologies are limited, while Graph Signal Processing (GSP)—a fast-developing field in the signal processing community—enables processing signals that reside on irregular domains and plays a critical role in numerous applications of geometric data from low-level processing to high-level analysis. To further advance the research in this field, we provide the first timely and comprehensive overview of GSP methodologies for geometric data in a unified manner by bridging the connections between geometric data and graphs, among the various geometric data modalities, and with spectral/nodal graph filtering techniques. We also discuss the recently developed Graph Neural Networks (GNNs) and interpret the operation of these networks from the perspective of GSP. We conclude with a brief discussion of open problems and challenges.

 

  • Related Publication

  •  Hu, W., Pang, J., Liu, X., Tian, D., Lin, C.-W., Vetro, A., "Graph Signal Processing for Geometric Data and Beyond: Theory and Applications", arXiv, August 2020.
    BibTeX arXiv
    • @article{Hu2020aug,
    • author = {Hu, Wei and Pang, Jiahao and Liu, Xianming and Tian, Dong and Lin, Chia-Wen and Vetro, Anthony},
    • title = {Graph Signal Processing for Geometric Data and Beyond: Theory and Applications},
    • journal = {arXiv},
    • year = 2020,
    • month = aug,
    • url = {https://arxiv.org/abs/2008.01918}
    • }