Papers
2
Total Citations
21
H-Index
2
About
Zhen Pan’s research lies at the intersection of advanced manufacturing and autonomous perception, with key contributions in weld pool monitoring and LiDAR-based scene understanding. In his highly cited 2018 work on image segmentation for robotic arc welding, Pan established robust methods to measure weld pool surface geometry using structured light, directly addressing the critical challenge of correlating surface geometry with weld penetration depth. This foundational work, with 19 citations, has informed real-time quality control in automated welding systems. More recently, Pan has pushed into autonomous driving and robotics with MosViT (2024), a novel vision transformer architecture designed for moving object segmentation from LiDAR point clouds. By effectively extracting spatial-temporal information from consecutive frames while tackling the scarcity of labeled training data, this work demonstrates his ability to bridge classical computer vision techniques with modern deep learning. Though still early in its impact, MosViT signals Pan’s growing influence in perception for dynamic environments. His trajectory—from precision manufacturing to autonomous systems—reflects a versatile researcher committed to solving practical sensing and segmentation challenges across domains.
Research Focus
Key Achievements
Top Papers
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