Yue Wang

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I am a Research Scientist at NVIDIA Research Autonomous Vehicle Research Group, working with Marco Pavone. I graduated from MIT EECS in 2022, advised by Justin Solomon at Geometric Data Processing Group. I was also fortunate to collaborate with Michael Bronstein and Phillip Isola. Previously, I was a master student at University of California, San Diego. Prior to that, I received my BEng in Computer Science from Zhejiang University. I’ve received the Nvidia Fellowship (2020-2021) and the MIT EECS William A. Martin Master’s Thesis Award (2021).

My research lies in the intersection of computer vision, computer graphics, and robotics. My goal is to use machine learning to enable robot intelligence with minimal human supervision. I study how to design 3D learning systems which leverage geometry, appearance, and any other cues that are naturally available in sensory inputs. I am also broadly interested in eclectic applications on top of these systems.

Topics I currently focus on include:

  • Neural fields for robotics and autonomous driving: FreeNeRF
  • Generative AI for 3D perception
  • Geometry-inspired self-supervised learning

Past topics:

We are organizing “Vision-Centric Autonomous Driving Workshop” at CVPR2023. Submit your recent papers, and participate in our novel challenges!


Feb 27, 2023 Four papers (few-shot neural rendering, representation learning for point clouds, end-to-end prediction for AV, and map learning) accepted to CVPR 2023!
Dec 30, 2022 Our workshop proposal “Data-Driven Visual Autonomous Driving (DDVAD)” has been accepted to CVPR2023. See you in Vancouver!
Sep 11, 2022 Two papers accepted to CORL 2022.
Sep 6, 2022 I joined Nvidia Research as a Research Scientist.

selected publications

  1. CVPR
    FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization
    Jiawei Yang, Marco Pavone, and Yue Wang
    In The Conference on Computer Vision and Pattern Recognition 2023
  2. CORL
    DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
    Yue Wang, Vitor Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin M. Solomon
    In The Conference on Robot Learning 2021
  3. ECCV
    Rethinking few-shot image classification: a good embedding is all you need?
    Yonglong Tian*, Yue Wang*, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola
    In The European Conference on Computer Vision 2020
  4. NeurIPS
    PRNet: Self-Supervised Learning for Partial-to-Partial Registration
    Yue Wang, and Justin M. Solomon
    In Conference on Neural Information Processing Systems 2019
  5. ICCV
    Deep Closest Point: Learning Representations for Point Cloud Registration
    Yue Wang, and Justin M. Solomon
    In The International Conference on Computer Vision 2019
  6. TOG
    Dynamic Graph CNN for Learning on Point Clouds
    Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon
    ACM Transactions on Graphics 2019