🥋 About me

I am Qianxin Qu, a Ph.D. student starting in Fall 2026 at the Hong Kong University of Science and Technology (Guangzhou), advised by Prof. Ying Cui. My research lies at the intersection of spatial intelligence and autonomous driving.

Before starting my Ph.D., I was a Research Assistant at the School of Vehicle and Mobility, Tsinghua University, mentored by Prof. Xinyu Zhang and Prof. Jun Li. I earned my B.Eng. in Computer Science and Technology from China University of Mining and Technology (CUMTB) in 2023, where I was guided by Prof. Jiajing Li. From 2022 to 2023, I also participated in joint training at Tsinghua University.

🦄 Research

My research focuses on spatial intelligence for multi-agent autonomous systems. I am interested in how connected vehicles and infrastructure can perceive, align, and reason about shared 3D environments. My previous work has centered on multi-agent traffic scenarios, especially V2X / CAV settings, where the key challenge is to turn heterogeneous observations into a reliable shared spatial representation. These projects have involved spatio-temporal alignment, calibration, localization, and cooperative perception.

Looking ahead, I hope to explore how 3D vision, Visual Language Models (VLMs), and 3D reconstruction foundation models can support spatial intelligence for multi-agent autonomous systems.

I also have a broader interest in Agentic AI, particularly spatial reasoning for physical-world agents and human-agent research workflows.

My current research interests include:

Spatial Intelligence: 3D vision, reconstruction foundation models, and geometry-aware representations;
V2X & Autonomous Driving: cooperative perception, connected autonomous systems;
Agentic AI: spatial reasoning for physical-world agents and human-agent research workflows.

📝 Publications

T-ITS 2025

V2X-Reg++: A Real-time Global Registration Method for Multi-End Sensing System in Urban Intersections

Xinyu Zhang*†, Qianxin Qu*, Yijin Xiong, Chen Xia, Ziqiang Song, Qian Peng, Kang Liu, Jun Li†, Keqiang Li

Note: This work was my independent research project, conducted under the auspices of Prof. Xinyu Zhang. I handled the entire research process, from literature review and concept development to methodological refinement, benchmark experiments, manuscript writing, revision, and the coordination of real-vehicle tests.

Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS, JCR Q1, IF:8.4)

tl;dr: We argue that current spatial alignment methods, which require an initial pose, are impractical for real-world Vehicle-to-Everything (V2X) cooperative perception. To address this limitation, we propose an online global registration algorithm that uses perception priors to align heterogeneous sensors in real-time.

IROS 2024 oral

V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems

Qianxin Qu*, Yijin Xiong*, Guipeng Zhang, Xin Wu, Xiaohan Gao, Xin Gao, Hanyu Li, Shichun Guo, Guoying Zhang†

IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), 2024

tl;dr: We re-examine the evolution of sensor calibration in V2I scenarios, highlighting the shift in demand from static, one-time calibration to dynamic, continuous alignment. We then propose an online, global registration of cross-source point cloud for algorithm for V2I.

IoT-J 2025

Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review

Yijin Xiong, Xinyu Zhang†, Xin Gao, Qianxin Qu, Chun Duan, Renjie Wang, Jing Liu, Jun Li†

Note: This survey was initiated by Dr. Yijin Xiong under the auspices of Prof. Xinyu Zhang. I later took responsibility for a substantial revision of the manuscript, expanding the literature coverage from 60+ to 120+ papers and reframing V2X calibration from a direct sensor-calibration problem to a broader spatial-alignment issue that extends into downstream cooperative perception.

IEEE Internet of Things Journal, 2025 (IoT-J, JCR Q1, IF:8.9)

tl;dr: This survey systematically organizes the evolution of sensor calibration from single-vehicle to cooperative intelligence.

T-IM 2023

Automated Extrinsic Calibration of Multi-Cameras and LiDAR

Xinyu Zhang†, Yijin Xiong, Qianxin Qu, Shifan Zhu, Shichun Guo, Dafeng Jin, Guoying Zhang, Haibing Ren, Jun Li†

Note: This research was initiated by Dr. Yijin Xiong under the auspices of Prof. Xinyu Zhang. It served as my undergraduate thesis; I proposed and improved the calibration algorithm and conducted the real-vehicle validation.

IEEE Transactions on Instrumentation and Measurement, 2023 (T-IM, JCR Q1, IF:5.9, Student First Author)

tl;dr: We propose an online, line-feature-based method to address extrinsic parameter drift in Camera-LiDAR systems during operation. Its real-world effectiveness was validated with industry partners (Meituan, MOGOX, and SAIC Motor).

T-ASE 2024

GF-SLAM: A Novel Hybrid Localization Method Incorporating Global and Arc Features

Yijin Xiong, Xinyu Zhang†, Wenju Gao, Jing Liu, Qianxin Qu, Shichun Guo, Yang Shen, Jun Li†

Note: This research was initiated by Dr. Yijin Xiong under the auspices of Prof. Xinyu Zhang. I was responsible for algorithm implementation and coordinating its real-world experiment.

IEEE Transactions on Automation Science and Engineering, 2024(T-ASE, JCR Q1, IF=6.4)

tl;dr: To address cumulative error in mapping for agricultural scenarios, we propose a robot localization method that fuses global and local environmental features. I was responsible for liaising with the Academy of Agricultural Sciences and implementing the real-world validation.