Zhaoxin Fan
Papers
2
Total Citations
114
H-Index
2
About
Zhaoxin Fan is a leading researcher in computer vision, with a primary focus on monocular object pose detection and tracking—a critical technology for autonomous driving, robotics, and augmented reality. His most influential work, the comprehensive overview "Deep Learning on Monocular Object Pose Detection and Tracking" (2022), has garnered 108 citations, establishing itself as a key reference in the field. In this survey, Fan systematically categorizes and evaluates deep learning approaches, highlighting their superiority over traditional methods and identifying open challenges. This contribution has helped shape the research agenda for pose estimation, providing both newcomers and experts with a structured understanding of the state of the art. Fan’s work is notable for its clarity and breadth, synthesizing advances across network architectures, loss functions, and evaluation metrics. With over 114 total citations, his research continues to influence the development of robust, real-time pose tracking systems. By bridging theoretical insights with practical applications, Zhaoxin Fan is helping to drive the next generation of intelligent perception systems that can accurately interpret 3D space from single images.
Research Focus
Key Achievements
Top Papers
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- 2