Yiqun Duan
Papers
1
Total Citations
6
H-Index
1
About
Yiqun Duan is an emerging researcher in computer vision, with a primary focus on stereo matching, 3D scene understanding, and depth estimation. His most notable contribution to date is the development of **OpenStereo** (2023), a comprehensive benchmark and strong baseline framework designed to systematically evaluate stereo matching architectures. This work addresses a critical gap in the field: with numerous competing methods emerging rapidly, researchers and practitioners lacked a unified, rigorous platform for fair comparison and architecture selection — particularly for applications in robotics and autonomous driving where accurate depth perception is essential. By creating a standardized evaluation environment, Duan's work empowers the research community to make more informed architectural decisions, accelerating progress in real-world deployment of stereo vision systems. OpenStereo has already garnered early citation traction, reflecting growing community interest in reproducible and comparable benchmarking practices. His research sits at the intersection of foundational computer vision methodology and practical engineering impact, making his work relevant to both academic researchers and industry practitioners building perception systems for autonomous agents. Duan represents a promising voice in the next generation of 3D vision researchers.
Research Focus
Key Achievements
Top Papers
- 1