Papers
2
Total Citations
16
H-Index
2
About
Wen-Nung Lie is a computer vision researcher whose work bridges classical 3D sensing and modern deep learning for object pose estimation. His early research established foundational methods in model-based recognition, using intensity-guided range sensing to identify and position polyhedra in 3D space—a contribution that remains cited decades later. More recently, Lie has advanced the state of the art in single-image pose estimation, developing techniques that are both faster and more precise for handling multiple instance objects. His 2022 paper on this topic, which has already garnered 11 citations, demonstrates his continued relevance in a rapidly evolving field. Lie’s career reflects a rare ability to span generations of vision technology, from geometric model matching to learning-based approaches, making him a valuable resource for students and researchers interested in the enduring challenge of robust object localization from visual data.
Research Focus
Key Achievements
Top Papers
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