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Personal profile

Research interests

Robotic Vision and Machine Learning, Photogrammetry and Remote Sensing, Augmented and Virtual Reality, with applications in Civil and Mechanical Engineering.


Dr. Chen Feng earned his Bachelor degree in geospatial engineering from Wuhan University in China. Then he went to the University of Michigan at Ann Arbor and earned a master degree in electrical engineering and a Ph.D. in civil engineering in 2015, where he studied robotic vision and learning and attempted to apply them in civil engineering. After graduation, he became a research scientist in the computer vision group at the Mitsubishi Electric Research Labs (MERL), focusing on visual SLAM and deep learning. In 2018 August, he became an assistant professor jointly in the Department of Mechanical and Aerospace Engineering and the Department of Civil and Urban Engineering in NYU Tandon School of Engineering, where his lab AI4CE aims to advance the robotic vision and learning with applications in civil/mechanical engineering.

Professional Affiliations


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Research Output

A scene-adaptive descriptor for visual SLAM-based locating applications in built environments

Xu, L., Feng, C., Kamat, V. R. & Menassa, C. C., Apr 2020, In : Automation in Construction. 112, 103067.

Research output: Contribution to journalArticle

  • Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering

    Chen, S., Duan, C., Yang, Y., Li, D., Feng, C. & Tian, D., Jan 1 2020, In : IEEE Transactions on Image Processing. 29, p. 3183-3198 16 p., 8931245.

    Research output: Contribution to journalArticle

  • An Occupancy Grid Mapping enhanced visual SLAM for real-time locating applications in indoor GPS-denied environments

    Xu, L., Feng, C., Kamat, V. R. & Menassa, C. C., Aug 1 2019, In : Automation in Construction. 104, p. 230-245 16 p.

    Research output: Contribution to journalArticle

  • Deepmapping: Unsupervised map estimation from multiple point clouds

    Ding, L. & Feng, C., Jun 2019, Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019. IEEE Computer Society, p. 8642-8651 10 p. 8954379. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2019-June).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Primitive fitting using deep geometric segmentation

    Li, D. & Feng, C., Jan 1 2019, p. 780-787. 8 p.

    Research output: Contribution to conferencePaper