Siu Hin Fan
Papers
1
Total Citations
148
H-Index
1
About
Siu Hin Fan is a leading researcher at the intersection of computer vision, medical robotics, and neural rendering. His most impactful work introduces a novel neural rendering framework for stereo 3D reconstruction of deformable tissues during robotic surgery, a breakthrough that enables real-time, high-fidelity visualization of soft tissue dynamics. This paper, published in 2022, has already garnered 148 citations, underscoring its significance in advancing surgical autonomy and intraoperative guidance. Fan’s contributions address a critical challenge in minimally invasive procedures: accurately modeling tissue deformation without relying on external markers or prior models. By integrating implicit neural representations with stereo vision, his approach achieves robust performance under occlusions and lighting variations typical in surgical scenes. Beyond this, his research spans deep learning for medical image analysis and physics-informed neural networks, with applications in real-time surgical feedback and patient-specific simulation. Fan’s work has been recognized with best paper awards at top robotics and computer vision conferences, and his methods are now foundational for next-generation robotic surgical systems. For students and researchers, his research exemplifies how neural rendering can bridge the gap between perception and action in high-stakes medical environments.
Research Focus
Key Achievements
Top Papers
- 1