Siheng Chen

Siheng Chen
  • Biography

    Before coming to MERL, Siheng worked postdoctoral research associate at CMU and on perception and prediction systems for self-driving cars at Uber Advanced Technologies Group. At CMU he received 2 masters degrees (one in Electrical \& Computer Engineering and one in Machine Learning) in addition to his PhD. He received his bachelor's degree in Electronics Engineering in 2011 from Beijing Institute of Technology, China. He is the recipient of the 2018 IEEE Signal Processing Society Young Author Best Paper Award. His coauthored paper received the Best Student Paper Award at 2018 IEEE Global Conference on Signal and Information Processing. His research interests include graph signal processing, graph neural networks, 3D point cloud processing, and graph mining.

  • Awards

    •  AWARD   MERL researcher wins IEEE Young Author Best Paper award
      Date: January 2, 2019
      Awarded to: Siheng Chen
      MERL Contact: Siheng Chen
      Research Area: Signal Processing
      Brief
      • MERL researcher, Siheng Chen, has won an IEEE Young Author Best Paper award for his paper entitled "Discrete Signal Processing on Graphs: Sampling Theory". This paper, published in the December 2015 issue of IEEE Transactions on Signal Processing, proposes a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory and shows that perfect recovery is possible for graph signals bandlimited under the graph Fourier transform. The award honors the authors of an especially meritorious paper dealing with a subject related to IEEE's technical scope and appearing in one if its journals within a three year window of eligibility.
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  • Internships with Siheng

    • CV1365: Machine learning for 3D computer vision

      MERL is looking for a self-motivated intern to work on machine learning for 3D computer vision. There are several available topics to choose from. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision. Proficiency in Python programming is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. Start date is flexible.

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  • MERL Publications

    •  Chen, S., Duan, C., Yang, Y., Feng, C., Li, D., Tian, D., "Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering", IEEE Transactions on Image Processing, January 2020.
      BibTeX Download PDFAbout TR2020-004
      • @article{Chen2020jan,
      • author = {Chen, Siheng and Duan, Chaojing and Yang, Yaoqing and Feng, Chen and Li, Duanshun and Tian, Dong},
      • title = {Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering},
      • journal = {IEEE Transactions on Image Processing},
      • year = 2020,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2020-004}
      • }
    •  Wu, Y., Marks, T., Cherian, A., Chen, S., Feng, C., Wang, G., Sullivan, A., "Unsupervised Joint 3D Object Model Learning and 6D Pose Estimation for Depth-Based Instance Segmentation", IEEE ICCV Workshop on Recovering 6D Object Pose, October 2019.
      BibTeX Download PDFAbout TR2019-118
      • @inproceedings{Wu2019oct,
      • author = {Wu, Yuanwei and Marks, Tim and Cherian, Anoop and Chen, Siheng and Feng, Chen and Wang, Guanghui and Sullivan, Alan},
      • title = {Unsupervised Joint 3D Object Model Learning and 6D Pose Estimation for Depth-Based Instance Segmentation},
      • booktitle = {IEEE ICCV Workshop on Recovering 6D Object Pose},
      • year = 2019,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2019-118}
      • }
    •  Chen, S., "Large-Scale 3D Point Cloud Representations via Graph Inception Networks with Applications to Autonomous Driving", Graph Signal Processing Workshop, June 2019.
      BibTeX Download PDFAbout TR2019-039
      • @inproceedings{Chen2019jun,
      • author = {Chen, Siheng},
      • title = {Large-Scale 3D Point Cloud Representations via Graph Inception Networks with Applications to Autonomous Driving},
      • booktitle = {Graph Signal Processing Workshop},
      • year = 2019,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2019-039}
      • }
    •  Duan, C., Chen, S., Tian, D., Moura, J., Kovacevic, J., "Deep Graph Topology Learning for 3D Point Cloud Reconstruction", Graph Signal Processing Workshop, June 2019.
      BibTeX Download PDFAbout TR2019-046
      • @inproceedings{Duan2019jun,
      • author = {Duan, Chaojing and Chen, Siheng and Tian, Dong and Moura, Jose and Kovacevic, Jelena},
      • title = {Deep Graph Topology Learning for 3D Point Cloud Reconstruction},
      • booktitle = {Graph Signal Processing Workshop},
      • year = 2019,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2019-046}
      • }
    •  Chen, S., Tian, D., Feng, C., Vetro, A., Kovacevic, J., "Fast Resampling of 3D Point Clouds via Graphs", IEEE Transactions on Signal Processing, DOI: 10.1109/TSP.2017.2771730, Vol. 66, No. 3, pp. 666-681, November 2017.
      BibTeX Download PDFAbout TR2017-215
      • @article{Chen2017nov,
      • author = {Chen, Siheng and Tian, Dong and Feng, Chen and Vetro, Anthony and Kovacevic, Jelena},
      • title = {Fast Resampling of 3D Point Clouds via Graphs},
      • journal = {IEEE Transactions on Signal Processing},
      • year = 2017,
      • volume = 66,
      • number = 3,
      • pages = {666--681},
      • month = nov,
      • doi = {10.1109/TSP.2017.2771730},
      • url = {https://www.merl.com/publications/TR2017-215}
      • }
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  • MERL Issued Patents

    • Title: "Methods and Systems for Fast Resampling Method and Apparatus for Point Cloud Data"
      Inventors: Tian, Dong; Feng, Chen; Vetro, Anthony; Chen, Siheng
      Patent No.: 10,229,533
      Issue Date: Mar 12, 2019
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