Papers
20
Total Citations
236
H-Index
9
About
Zhe Min is a computational researcher whose work bridges medical robotics, computer-assisted surgery, and autonomous robotic systems. His most significant contributions lie in the domain of point set and point cloud registration, where he has developed sophisticated probabilistic frameworks that advance the state of the art in computer-assisted orthopedic surgery (CAOS). Notably, his generalized registration algorithms incorporating hybrid mixture models and anisotropic positional uncertainties—accumulated over 35 and 26 citations respectively—address a fundamental challenge: accurately aligning preoperative and intraoperative scans to enable precise surgical interventions. By extending classical methods like Coherent Point Drift to handle both positional and orientational information under realistic noise conditions, Min has meaningfully improved registration robustness across rigid, nonrigid, and curve-to-surface scenarios. Beyond surgical applications, Min has demonstrated breadth by contributing to semantic mapping for mobile robots in dynamic environments, autonomous elevator-button operation systems, and airport trolley deployment robotics. His 2025 work exploring large vision models for robot-assisted surgery signals an evolving focus on harnessing foundation AI models in clinical settings. With over 180 cumulative citations across a decade of output, Zhe Min represents a productive voice at the intersection of probabilistic modeling, computer vision, and intelligent medical robotics—making his work valuable reading for students pursuing surgical automation or robotic perception research.
Research Focus
Key Achievements
Top Papers
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- 3Innovating robot-assisted surgery through large vision models24 citations · 2025
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