Ye Li

I am a first-year Ph.D. student in Computer Science and Engineering at the University of Michigan, Ann Arbor, advised by Prof. Stella X. Yu.

Previously, I received my M.S. in Robotics from the University of Michigan, Ann Arbor in 2025, advised by Prof. Xiaonan (Sean) Huang. In 2024, I visited the BAIR at UC Berkeley, working with Prof. Kurt Keutzer. I received my bachelor’s degree in 2023 from the College of Automotive Engineering at Jilin University.

I am open to discussions and collaborations in robotics and autonomous driving. Feel free to drop me an email if you find our research a potential match.

Email  /  Google Scholar  /  Github  /  LinkedIn

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News

2025/05 - We are organizing the RoboSense Challenge at IROS 2025!
2025/01 - Two papers were accepted at ICLR 2025!
2024/09 - One paper was accepted at NeurIPS 2024 as a spotlight!
2024/05 - Invited talk at the RoboDrive Challenge, ICRA 2024.
2024/01 - One paper was accepted at ICRA 2024!
2023/09 - We are organizing Tensegrity Robotics Workshop at IROS 2023!



Selected Publications


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Is Your LiDAR Placement Optimized for 3D Scene Understanding?

Ye Li, Lingdong Kong, Hanjiang Hu, Xiaohao Xu, Xiaonan Huang
Advances in Neural Information Processing Systems (NeurIPS), 2024. [Spotlight]
[paper]
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UniDrive: Towards Universal Driving Perception Across Camera Configurations

Ye Li, Wenzhao Zheng, Xiaonan Huang, Kurt Keutzer
International Conference on Learning Representations (ICLR), 2025.
[paper]
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Influence of Camera-LiDAR Configuration on 3D Object Detection for Autonomous Driving

Ye Li*, Hanjiang Hu*, Zuxin Liu, Xiaohao Xu, Xiaonan Huang, Ding Zhao
2024 IEEE International Conference on Robotics and Automation (ICRA)
[paper]
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A new scheme of vehicle detection for severe weather based on multi-sensor fusion

Zhangu Wang, Jun Zhan, Ye Li, Zhaohui Zhong, Zikun Cao
Measurement, Volume 191, 2022, 110737
[paper]
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Learning Shared RGB-D Fields: Unified Self-supervised Pre-training for Label-efficient LiDAR-Camera 3D Perception

Xiaohao Xu, Ye Li, Tianyi Zhang, Jinrong Yang, Matthew Johnson-Roberson, Xiaonan Huang
2025 IEEE International Conference on Automation Science and Engineering
[paper]
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Scalable Benchmarking and Robust Learning for Noise-Free Ego-Motion and 3D Reconstruction from Noisy Video

Xiaohao Xu, Tianyi Zhang, Shibo Zhao, Xiang Li, Sibo Wang, Yongqi Chen, Ye Li, Bhiksha Raj, Matthew Johnson-Roberson, Sebastian Scherer, Xiaonan Huang
International Conference on Learning Representations (ICLR), 2025.
[paper]