Shuquan Ye
Papers
1
Total Citations
3
H-Index
1
About
Shuquan Ye is a leading researcher in computer vision and autonomous systems, with a focus on solving perception challenges posed by transparent and reflective surfaces. His most cited work, "Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection" (2025), addresses a critical gap in autonomous navigation: the inability of standard sensors to reliably detect glass panels, which are increasingly common in modern architecture. By developing a cross-modal context mining framework that fuses RGB and depth data, Ye’s method significantly improves glass surface detection accuracy, enabling safer operation for robots, self-driving cars, and drones. With 3 citations already, this work is gaining traction for its practical impact on real-world autonomy. Ye’s contributions extend to advancing scene understanding in challenging environments, where traditional visual cues fail. His research not only enhances the reliability of autonomous systems but also opens new avenues for handling transparent obstacles—a problem long overlooked in computer vision. For students and researchers, Ye’s work exemplifies how targeted, application-driven research can solve tangible problems, bridging the gap between laboratory algorithms and field-ready technology.
Research Focus
Key Achievements
Top Papers
- 1