Juseong Lee
Papers
1
Total Citations
2
H-Index
1
About
Juseong Lee is a researcher specializing in 3D environment modeling, autonomous navigation, and robotics perception, with a particular focus on robust plane extraction from point cloud data. His most-cited work, "Robust Plane Extraction using Supplementary Expansion for Low-Density Point Cloud Data" (2018), addresses a critical challenge in 3D object manipulation and autonomous systems: the inaccuracy of conventional plane extraction methods when dealing with sparse, low-density point clouds. Lee’s key contribution lies in developing a supplementary expansion technique that improves the robustness and accuracy of plane detection, overcoming limitations of traditional decomposing and merging approaches. This work has garnered attention in the field, with 2 citations to date, and is foundational for applications requiring reliable 3D environment modeling. Lee’s research bridges the gap between theoretical geometry processing and practical robotics, enabling more precise spatial understanding for autonomous vehicles and robotic manipulators. His achievements highlight a commitment to solving real-world perception problems, making his work valuable for students and researchers advancing in point cloud processing and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1