Yueh-Ying Lee
Papers
1
Total Citations
29
H-Index
1
About
Yueh-Ying Lee is a computer vision researcher whose work centers on 6DoF (six degree-of-freedom) object pose tracking, a critical challenge for augmented reality and robotics applications. Her most-cited contribution, the 2017 paper "[POSTER] A Benchmark Dataset for 6DoF Object Pose Tracking," has garnered 29 citations and addresses a fundamental gap in the field: the lack of standardized evaluation tools for comparing pose-tracking algorithms in real-world scenes. By introducing a carefully curated benchmark dataset, Lee provided the research community with a rigorous framework to assess and advance tracking methods, enabling more accurate and reliable performance in dynamic environments. This work has directly supported progress in AR interfaces and robotic manipulation, where precise object localization is essential. Lee’s contributions are notable for their practical impact—her dataset has become a reference point for subsequent studies, helping to establish best practices in pose estimation. Through her methodical approach to benchmarking, she has strengthened the foundations of 6DoF tracking, making her a key figure in bridging the gap between algorithmic development and real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1[POSTER] A Benchmark Dataset for 6DoF Object Pose Tracking29 citations · 2017