Papers
3
Total Citations
676
H-Index
3
About
Jun Xie is a versatile researcher whose work spans computer vision, autonomous robotics, and human-robot interaction — fields that sit at the exciting intersection of artificial intelligence and real-world intelligent systems. Perhaps his most significant contribution is his involvement in the development of **KITTI-360**, a landmark dataset and benchmarking suite for urban scene understanding in both 2D and 3D environments. Published in 2022, this work has already garnered an impressive 630 citations, underscoring its foundational role in advancing research on self-driving vehicles and embodied AI systems that bridge computer vision, graphics, and robotics. Beyond large-scale dataset construction, Xie has made meaningful contributions to mobile robot navigation, proposing a hybrid path-planning method that fuses the A* algorithm with the Dynamic Window Approach to achieve both global optimality and real-time obstacle avoidance. More recently, he has pushed into the frontier of natural language-guided robotic manipulation, developing multisensory perception frameworks that enable robots to interpret human instructions more naturally and reliably. Taken together, Xie's research portfolio reflects a coherent vision: building intelligent systems that perceive, navigate, and communicate seamlessly within complex, real-world environments.
Research Focus
Key Achievements
Top Papers
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