Jianye Yi
Papers
1
Total Citations
4
H-Index
1
About
Jianye Yi is a researcher specializing in computer vision and robotics, with a particular focus on robust pose estimation and keypoint detection. His most notable contribution is the development of G-SAM, a one-shot keypoint detection framework designed to enhance the accuracy and reliability of Perspective-n-Point (PnP) based robot pose estimation. This work, published in 2023, addresses critical challenges in real-world robotic applications, such as occlusion and lighting variations, by leveraging a graph-based attention mechanism to improve keypoint matching. Although early in its publication cycle, G-SAM has already garnered 4 citations, signaling its potential impact on the field. Yi’s research bridges the gap between theoretical computer vision algorithms and practical robotic systems, offering solutions that are both efficient and robust. His work is particularly relevant for autonomous navigation, manipulation, and industrial automation, where precise pose estimation is essential. As a rising researcher, Yi’s contributions are poised to influence future advancements in robotic perception and control.
Research Focus
Key Achievements
Top Papers
- 1