Papers
2
Total Citations
50
H-Index
2
About
Tianbo Pan is a leading researcher in computer vision and robotics, specializing in event-based vision and depth estimation. His work focuses on leveraging bio-inspired event cameras—sensors that capture asynchronous intensity changes with high temporal resolution and dynamic range—to overcome the limitations of traditional frame-based cameras in challenging environments. Pan’s major contributions include a comprehensive survey and benchmark on deep learning for event-based vision, which has garnered 39 citations since 2023, providing a foundational resource for the field. He also developed SRFNet, a novel framework for monocular depth estimation that integrates frames and events through spatial reliability-oriented fusion, achieving fine-grained structural accuracy. This work, cited 11 times in 2024, addresses critical issues like motion blur and low dynamic range, advancing applications in robot navigation and autonomous driving. Pan’s research has significant impact, with his papers collectively cited over 50 times, reflecting their influence on both academic and practical domains. His notable achievements include pioneering fusion techniques that set new standards for robustness in real-world vision systems, making him a key figure in the evolution of event-based perception.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks39 citations · 2023
- 2