Papers
3
Total Citations
110
H-Index
3
About
M. Ye is a pioneering researcher at the intersection of robotic surgery and autonomous navigation, whose work has significantly advanced both fields. His most impactful contribution comes from the development of self-supervised Siamese learning on stereo image pairs for depth estimation in robotic surgery, a paper that has garnered 104 citations and laid crucial groundwork for enhancing 3D perception in minimally invasive procedures using the da Vinci surgical platform. This work addresses the critical challenge of incorporating preoperative information into real-time surgical vision, improving precision and safety in robotic-assisted operations. In parallel, Ye has made notable strides in visual robot place recognition, focusing on the efficiency and accuracy of 3D point cloud processing from LiDAR sensors. His research on scan-context descriptors, combined with dictionary-based coding and CNN-SVM frameworks, has pushed the boundaries of how robots self-localize in complex environments. By bridging surgical robotics and autonomous navigation, Ye demonstrates a unique ability to transfer computer vision and deep learning techniques across domains, establishing himself as a versatile innovator in intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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