Zekai Liang
Papers
2
Total Citations
4
H-Index
2
About
Zekai Liang is an emerging researcher working at the intersection of computer vision, robotics, and medical imaging. His work focuses on two compelling and highly relevant domains: surgical scene reconstruction and camera-to-robot pose estimation, both of which are foundational to the future of autonomous robotic surgery and vision-based robot control. In his notable work BASED, Liang tackles one of the most challenging problems in surgical robotics — reconstructing deformable, dynamic scenes from endoscopic video using Neural Radiance Fields with bundle adjustment. This capability is critical for intraoperative navigation and minimally invasive surgical automation. Complementing this, his CtRNet-X framework advances markerless camera-to-robot calibration, enabling accurate pose estimation under real-world conditions without cumbersome physical setups — a meaningful step toward practical robotic deployment. Though early in his research career, with his 2025 publications each accumulating 2 citations, Liang is addressing problems of significant consequence to the surgical robotics and computer vision communities. Students interested in neural rendering, robotic perception, or medical robotics will find his work a valuable entry point into cutting-edge challenges shaping the next generation of intelligent surgical systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2