Papers
14
Total Citations
247
H-Index
9
About
Francisco Vasconcelos is a researcher whose work sits at the intersection of computer vision, robotics, and surgical technology, with a particular focus on advancing the capabilities of robotic-assisted and laparoscopic surgery. His most significant contributions lie in hand-eye calibration, where he has developed multiple algorithms — including adjoint transformation and screw-constraint approaches — that precisely determine the geometric relationship between robotic arms and cameras, foundational to reliable surgical robotics. These works have collectively garnered over 80 citations, reflecting their strong uptake in the robotics community. Beyond calibration, Vasconcelos has made notable strides in surgical scene understanding, developing methods for smoke removal in laparoscopic video, multitask stereo disparity estimation paired with instrument segmentation, and simulation-supervised learning for instrument detection — each addressing practical challenges that limit automation in the operating room. His 2023 SurgT benchmark further demonstrates a commitment to community-driven evaluation of soft-tissue tracking. His body of work also extends to SLAM under remote centre of motion constraints and automated needle pick-up for suturing assistance. Collectively, his research pushes robotic surgery closer to reliable, intelligent autonomy, making him a significant contributor to the field of computer-assisted interventions.
Research Focus
Key Achievements
Top Papers
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- 4Hand-Eye Calibration With a Remote Centre of Motion26 citations · 2019
- 5SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery23 citations · 2023
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- 9Automated Pick-Up of Suturing Needles for Robotic Surgical Assistance13 citations · 2018
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