Papers
5
Total Citations
62
H-Index
5
About
Xurui Li is a researcher whose work bridges the frontiers of robotic automation and surgical innovation. His primary research areas include digital twin modeling for smart manufacturing, 3D scene understanding for robotics, and the clinical application of robot-assisted surgery. Li’s major contributions are twofold: in engineering, he developed a digital twin-based assembly strategy for precision PCB kit-box builds (22 citations) and pioneered an energy-optimized point cloud segmentation method for efficient 3D scene understanding (8 citations), enhancing robotic object manipulation. In medicine, he led meta-analyses comparing single-port versus multiple-port robot-assisted pyeloplasty (19 citations) and evaluating the impact of prior transurethral resection on robotic prostatectomy outcomes (8 citations), providing critical evidence for surgical decision-making. His saliency-driven multi-scale resampling approach for RGB-D point clouds (5 citations) further advances robotic scene representation. Li’s work uniquely integrates engineering precision with clinical efficacy, demonstrating high impact through citations across both fields. His achievements highlight a rare interdisciplinary expertise, offering valuable insights for students and researchers in robotics, manufacturing, and urological surgery.
Research Focus
Key Achievements
Top Papers
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