Seungryong Kim
Papers
1
Total Citations
5
H-Index
1
About
Seungryong Kim is a researcher whose work lies at the intersection of computer vision and robotics, with a particular focus on 6D object pose estimation—a critical capability for enabling robots to perceive and interact with their environment. His most notable contribution, "6D Object Pose Estimation Using a Particle Filter With Better Initialization" (2023), addresses a fundamental challenge in robotic grasping: how to accurately determine an object's position and orientation in three-dimensional space without relying on expensive, labor-intensive ground truth annotations. By integrating a particle filter with an improved initialization strategy, Kim's approach offers a practical, learning-based solution that reduces the dependency on large annotated datasets, making it more viable for real-world robotic applications. While his work has garnered 5 citations to date, its significance lies in bridging the gap between high-performance deep learning methods and the constraints of physical robot environments. Kim's research is particularly valuable for students and engineers working on autonomous manipulation, as it provides a pathway to deploy robust pose estimation in settings where annotated data is scarce, advancing the frontier of practical, deployable robotics.
Research Focus
Key Achievements
Top Papers
- 1