Yuen Hei Yeung
Papers
1
Total Citations
8
H-Index
1
About
Yuen Hei Yeung is a researcher at the forefront of computer vision and robotics, specializing in the perception of challenging transparent and reflective surfaces. His work directly addresses a critical bottleneck for autonomous systems: the safe navigation of environments filled with glass panels, which are notoriously invisible to standard sensors. Yeung’s major contribution is the development of novel deep learning architectures that leverage RGB-D data for robust glass surface detection. His most-cited paper, "Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection" (2022), has already garnered 8 citations, demonstrating its immediate impact on the field. By enabling robots, self-driving cars, and drones to reliably perceive these transparent obstacles, Yeung is solving a fundamental problem that has long plagued autonomous navigation in modern urban and indoor settings. His work is not only technically innovative but also highly practical, paving the way for safer and more capable autonomous systems in environments increasingly dominated by glass architecture.
Research Focus
Key Achievements
Top Papers
- 1