Xinshuo Weng

Ph.D., Robotics Institute
School of Computer Science
Carnegie Mellon University

Office: Smith Hall 210
Email: xinshuow@cs.cmu.edu

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Xinshuo Weng is a Ph.D. student (2018-) at Robotics Institute of Carnegie Mellon University (CMU) advised by Kris Kitani. She received master's degree (2016-17) at CMU, where she worked with Yaser Sheikh and Kris Kitani. Prior to CMU, she worked at Facebook Reality Lab as a research engineer to help build “Photorealistic Telepresence”. Her bachelor was received from Wuhan University. Her recent research interest lies in 3D computer vision and Graph Neural Networks for autonomous systems. She has developed some 3D multi-object tracking systems such as AB3DMOT that received >1,000 stars on GitHub. Also, she is leading a few autonomous driving workshops at conferences such as NeurIPS 2020, IJCAI 2021, ICCV 2021. She was awarded a Qualcomm Innovation Fellowship for 2020-2021.

Fields: Computer Vision, Machine Learning, Robotics, Multimedia
Topics: 3D Vision, Point Cloud Processing, Graph Neural Networks, Generative Modeling

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Highlights

News

  • 04/2021 - Organizing ICCV 2021 Workshop on Multi-Agent Interaction and Relational Reasoning
  • 03/2021 - Organizing IJCAI 2021 Workshop on Artificial Intelligence for Autonomous Driving
  • 03/2021 - One paper accepted at CVPR 2021
  • 02/2021 - Two papers accepted at ICRA 2021
  • 12/2020 - Invited speaker at Wayve and Computer Vision Talks
  • 12/2020 - Honored to receive the Outstanding Reviewer Award at ACCV 2020
  • 10/2020 - Papers accepted at CoRL 2020, WACV 2021, ISARC 2020
  • 08/2020 - Honored to receive the Qualcomm Innovation Fellowship 2020
  • 08/2020 - Keynote speaker at ECCV 2020 Workshop on Benchmarking Trajectory Forecasting Models [Slides]
  • 08/2020 - Organizing NeurIPS 2020 Workshop on Machine Learning for Autonomous Driving
  • 08/2020 - Four (one oral, three spotlight) papers accepted at ECCV 2020 Workshops
  • 06/2020 - Two papers accepted at IROS 2020
  • 06/2020 - Keynote Speaker at CVPR 2020 Workshop on Scalibility in Autonomous Driving [Slides] [Video]
  • 04/2020 - One paper accepted at TPAMI 2020
  • 03/2020 - One paper accepted at CVPR 2020
    • See More
  • 08/2019 - One paper accepted at ICCV Workshops 2019
  • 06/2019 - We release the code for our 3D MOT paper here
  • 06/2019 - Three papers accepted at BMVC 2019, ACMMM 2019, IROS 2019
  • 01/2018 - One paper accepted at CVPR 2018
  • 10/2017 - One paper accepted at WACV 2018
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    Selected Works [Full Publications] [Google Scholar]

    PTP: Parallelized Tracking and Prediction with Graph Neural Networks and Diversity Sampling
    Xinshuo Weng*, Ye Yuan*, Kris Kitani
    Robotics and Automation Letters (RA-L), 2021
    with presentation at IEEE International Conference on Robotics and Automation (ICRA), 2021
    PDF | Code | Website | BibTex
    The first parallelized 3D MOT and trajectory forecasting method with object interaction modeling and diverse trajectory samples

    Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting
    Xinshuo Weng, Jianren Wang, Sergey Levine, Kris Kitani, Nick Rhinehart
    Conference on Robot Learning (CoRL), 2020
    PDF | Code | Demo | Website | Slides | BibTex | Supp
    By learning to forecast future LiDAR point clouds, we build a forecast-then-detect pipeline for pose forecasting by reversing the steps of detection and forecasting


    GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with 2D-3D Multi-Feature Learning
    Xinshuo Weng, Yongxin Wang, Yunze Man, Kris Kitani
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
    PDF | Code | Demo | Website | Slides | BibTex
    The first multi-object tracking method that leverages Graph Neural Network for object interaction modeling

    3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
    Xinshuo Weng, Jianren Wang, David Held, Kris Kitani
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
    PDF | Code | Demo | Website | Slides | BibTex
    A 3D multi-object tracker that achieves state-of-the-art performance with the fastest speed

    Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud
    Xinshuo Weng, Kris Kitani
    IEEE International Conference on Computer Vision (ICCV) Workshops, 2019.
    PDF | Code | Poster | BibTex
    By projecting the 2D image to a pseudo-LiDAR point cloud representation, our monocular 3D detection pipeline quadruples the performance over prior art

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