Hojin Yoo
Papers
1
Total Citations
5
H-Index
1
About
Hojin Yoo is a researcher advancing the field of 3D computer vision, with a focus on point cloud analysis for autonomous driving and robotics. His major contribution lies in tackling the critical problem of domain adaptation in point cloud classification—specifically, the challenge where sensor discrepancies create a domain gap between training and real-world data. In his most cited work (2023, 5 citations), Yoo introduced innovative techniques such as Recycling Max Pooling and Cutting Plane Identification to improve deep model robustness across different sensor domains. This work addresses a fundamental bottleneck in deploying perception systems reliably in dynamic environments. Though early in his career, Yoo’s research demonstrates a clear commitment to bridging the gap between synthetic and real-world point cloud data, a key step toward safer and more adaptable autonomous systems. His contributions are particularly relevant for students and researchers working on domain shift, 3D scene understanding, and sensor-agnostic deep learning.
Research Focus
Key Achievements
Top Papers
- 1