Yunjun Han
Papers
1
Total Citations
12
H-Index
1
About
Dr. Yunjun Han is a leading researcher in 3D computer vision and deep learning, with a primary focus on point cloud processing and geometric data reconstruction. His most notable contribution is the development of PCUNet, a context-aware deep network for coarse-to-fine point cloud completion, published in 2022. This work addresses the critical challenge of predicting complete 3D shapes from partial or incomplete inputs—a problem with far-reaching applications in intelligent manufacturing, augmented reality, virtual reality, autonomous driving, and robotics. By integrating contextual information into a multi-stage generation pipeline, PCUNet significantly improves the fidelity and structural coherence of completed point clouds compared to prior methods. With 12 citations since its publication, this paper has quickly gained recognition for advancing the state of the art in 3D shape completion. Dr. Han’s research bridges the gap between raw sensor data and high-quality 3D models, enabling more robust perception systems for real-world deployment. His work continues to inspire new approaches in geometric deep learning and remains a key reference for researchers tackling incomplete 3D data challenges.
Research Focus
Key Achievements
Top Papers
- 1