Jieqi Shi

Jieqi(Jaycee) Shi
Contact: jshias AT connect DOT ust DOT hk

I am currently a postdoc researcher in UAV Group of the Hong Kong University of Science and Technology. Before that, I spent four wonderful years in HKUST and received my PhD degree in Jan 2024, supervised by Prof. Shaojie Shen. I received my bachelor's degree in School of Electronic Engineering and Computer Science, Peking University. I was a research intern at Carnegie Mellon University, Megvii. Co, and Dji Automotive.

My research interests lie in 3D vision, robotics and artificial intelligence.

I take wide part in activities. Reading, piano and travelling have given me so much fun in my life. I am also fond of animation and games, such as Naruto, Bleach, Hunter x Hunter and many others. I will be really glad if anyone talk with me about my projects or share with me his or her interets through e-mail.

June 2022: Happy to receive the HKUST Redbird Academic Excellence Award for Continuning Phd Students (2021/22)!

Publications


J. Shi, P. Li, X. Chen and S. Shen. You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning. IEEE Robotics and Automation Letters, 8(10), pp. 6811-6818, August 2023.

C. Liu, K. Wang, J. Shi, Z. Qiao and S. Shen. FM-fusion: instance-aware semantic mapping boosted by vision-language foundation models. IEEE Robotics and Automation Letter, 1(8), pp. 0-0, January 2024.

J. Shi, P. Li, X. Chen and S. Shen. Are all point clouds suitable for completion? Weakly supervised quality evaluation network for point cloud completion. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), London, UK, May 2023.

D. Yan, X. Lyu, J. Shi and Y. Lin. Efficient Implicit Neural Reconstruction Using LiDAR. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), London, UK, May 2023.

J. Shi, L. Xu, P. Li, X. Chen and S. Shen. Temporal point cloud completion with pose disturbance. IEEE Robotics and Automation Letters, January 2022.

J. Shi, L. Xu, L. Heng and S. Shen. Graph guided deformation for point cloud completion. IEEE Robotics and Automation Letters, 6(4), pp. 7081-7088, July 2021.

J. Shi, P. Li and S. Shen. Tracking from patterns: learning corresponding patterns in point clouds for 3D object tracking. In Proc. of the European Conference on Computer Vision (ECCV) Workshop on Perception for Autonomous Driving (PAD), virtual, August 2020.

P. Li, J. Shi and S. Shen. Joint spatial-temporal optimization for stereo 3D object tracking. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6876-6885, Seattle, Washington, USA, June 2020.

Z. Liao, J. Shi, X. Qi, X. Zhang, W. Wang, Y. He, R. Wei and X. Liu. Coarse-To-Fine Visual Localization Using Semantic Compact Map. 2020 3rd International Conference on Control and Robots (ICCR), pages 30-37, 2020.

W. Dong, J. Shi, W. Tang, X. Wang and H. Zha. An Efficient Volumetric Mesh Representation for Real-time Scene Reconstruction using Spatial Hashing. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pages 6323-6330, 2018

Y. Wu, C. Li and J. Shi. Smart RF Table Enables IoT on a Desk. In Proc. of the 23rd Annual International Conference on Mobile Computing and Networking(MobiCom), n. page, 2017

My Work

  • J. Shi, L. Xu, L. Heng and S. Shen. Graph guided deformation for point cloud completion. RAL with IROS 2021 accepted;
  • J. Shi, L. Xu, P. Li, X. Chen and S. Shen. Temporal point cloud completion with pose disturbance. IEEE Robotics and Automation Letters, January 2022.
  • Use subsequent patterns of lidar point clouds to do association in the detection network.
  • W. Dong, J. Shi, W. Tang, X. Wang and H. Zha. An Efficient Volumetric Mesh Representation for Real-time Scene Reconstruction using Spatial Hashing. ICRA 2018 accepted;
  • A 3D object detection and tracking pipeline.
  • A coarse-to-fine localization method with light-weight maps.
  • Fast Parametric Shape Detection Based on Curvilinear Differential Structure and Optimization
  • Independent ‘teacher’ models of different functions, like hat recognition, upper body segmentation and shoes division, would keep labeling the same input data with all labels we need, which were later used to train an integrated student model.
  • Y. Wu, C. Li and J. Shi. Poster: Smart RF Table Enables IoT on a Desk. Mobicom17 poster accepted; Mobicom17 student research competition second place
  • An Android application helps users to integrate sporadic and fragmented time so that they can achieve a relatively long-term goal for personalized learning and entertaining.

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