Sainan Liu
Papers
2
Total Citations
4
H-Index
2
About
Sainan Liu is an emerging researcher working at the intersection of computer vision, robotics, and medical imaging, with a particular focus on surgical perception and robot-robot calibration systems. Liu's work addresses some of the most technically demanding challenges in modern robotic surgery and autonomous intervention, developing novel approaches that push the boundaries of what is computationally achievable in dynamic, real-world environments. Among Liu's most notable contributions is BASED, a pioneering framework that applies Neural Radiance Fields to reconstruct deformable scenes from endoscopic video footage — a critical capability for intraoperative navigation and minimally invasive surgical robotics. Complementing this, Liu's CtRNet-X advances markerless camera-to-robot pose estimation, enabling accurate robot calibration under real-world conditions using only a single camera, eliminating cumbersome physical setup requirements that have long hindered practical deployment. Both works have already garnered early citation attention, reflecting their relevance to active research communities in surgical robotics and computer vision. Though Liu's publication record is still developing, the sophistication and applicability of these contributions suggest a researcher poised to make lasting impact in intelligent surgical systems and vision-based robotic control.
Research Focus
Key Achievements
Top Papers
- 1
- 2