Papers
4
Total Citations
20
H-Index
3
About
Zhen Chen is a versatile computer vision and robotics researcher whose work spans autonomous navigation, surgical intelligence, industrial automation, and scene understanding. His research is unified by a commitment to developing practical, real-world AI systems that bridge perception and action across diverse domains. Chen's most recognized contribution, his 2020 work on monocular depth estimation, demonstrated how transfer learning combined with surface normal guidance could enable lightweight CNN architectures to achieve high-quality 3D scene reconstruction — a critical capability for drone and robotic navigation systems. More recently, he has pushed into the rapidly evolving field of surgical AI, developing Surgical-LVLM, a large vision-language model adapted for grounded visual question answering in robotic surgery, addressing the pressing need for intelligent, context-aware surgical mentorship systems. His industrial robotics work introduces innovative Run Length Encoding-based weld seam detection from point clouds, directly improving welding robot efficiency in complex shipbuilding environments. Additional contributions to pedestrian detection further reflect his broad expertise in safety-critical perception systems. With citations spanning multiple high-impact domains, Chen exemplifies the kind of interdisciplinary researcher whose foundational work in computer vision is actively shaping the next generation of intelligent machines in both medical and industrial settings.
Research Focus
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Top Papers
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