Papers
2
Total Citations
19
H-Index
2
About
Zaipeng Duan is a researcher at the forefront of autonomous driving and robotic perception, specializing in multi-sensor fusion and cross-modal learning. His primary research areas include LiDAR-camera calibration and semantic segmentation, where he addresses critical challenges in enabling reliable perception for self-driving vehicles and robots. Duan’s most notable contribution is a robust LiDAR-camera self-calibration method that leverages rotation-based alignment and a multi-level cost volume, eliminating the need for laborious manual calibration and achieving 12 citations since 2023. This work is pivotal for multi-sensor collaborative perception, a key trend in autonomous navigation. Additionally, his transformer-based cross-modal information fusion network for semantic segmentation, with 7 citations, demonstrates his expertise in integrating data from different sensors to enhance scene understanding. Duan’s research not only advances the accuracy and efficiency of sensor calibration but also pushes the boundaries of how machines interpret complex environments. His achievements are essential for students and researchers exploring the intersection of computer vision, deep learning, and robotics, offering practical solutions for real-world autonomous systems.
Research Focus
Key Achievements
Top Papers
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