Papers
2
Total Citations
28
H-Index
2
About
Bin Fan is a leading researcher in computer vision, with a primary focus on rolling shutter camera modeling, optimization, and learning. His work addresses fundamental challenges in geometric computer vision, particularly the distortions and asynchronous capture inherent in rolling shutter sensors—a ubiquitous technology in modern smartphones and consumer cameras. Fan's major contributions include developing novel mathematical frameworks that unify rolling shutter geometry with traditional structure-from-motion (SfM) pipelines, enabling accurate 3D reconstruction and image correction from single and stereo rolling shutter cameras. His highly cited 2023 monograph, *Rolling Shutter Camera: Modeling, Optimization and Learning*, has garnered 18 citations and serves as a definitive resource for researchers and practitioners. Additionally, his 2022 paper on differential SfM for rolling shutter stereo rigs (10 citations) introduced efficient correction techniques that have been adopted in real-time applications. Fan's work bridges theoretical rigor with practical deployment, influencing fields from autonomous navigation to augmented reality. His research continues to shape how the vision community handles temporal sampling artifacts, making him a key figure in advancing robust, real-world computer vision systems.
Research Focus
Key Achievements
Top Papers
- 1Rolling Shutter Camera: Modeling, Optimization and Learning18 citations · 2023
- 2Differential SfM and image correction for a rolling shutter stereo rig10 citations · 2022