Qingyi Sun
Papers
1
Total Citations
9
H-Index
1
About
Qingyi Sun is a researcher whose work lies at the intersection of computer vision and robotics, with a particular focus on advancing 6D object pose estimation—a critical capability for enabling autonomous systems to perceive and interact with their environments. Their most cited paper, “Efficient 6D object pose estimation based on attentive multi‐scale contextual information” (2022, 9 citations), tackles the persistent challenge of achieving accurate pose estimation under complex, real-world conditions such as variable illumination and cluttered scenes. Sun’s key contribution is the introduction of an attention-driven, multi-scale contextual framework that enhances both the robustness and efficiency of pose estimation, making it more suitable for deployment in service robots, collaborative robots, and unmanned warehouses. By addressing the trade-off between accuracy and computational cost, this work has practical implications for the next generation of intelligent robotic systems. Though early in their career, Sun’s research demonstrates a clear commitment to solving foundational problems in perception for robotics, and their work is already gaining recognition as a valuable resource for researchers and engineers developing autonomous manipulation and navigation technologies.
Research Focus
Key Achievements
Top Papers
- 1