Papers
80
Total Citations
1,837
H-Index
24
About
Qi Dou is a prominent researcher whose work bridges computer vision, medical image analysis, and surgical robotics, with particular expertise in 6D object pose estimation, surgical scene understanding, and robot-assisted surgery. Her contributions span two deeply intertwined domains: advancing perception systems for robotic manipulation and developing intelligent tools for minimally invasive surgical assistance. Among her most influential work, Dou has tackled the challenging problem of category-level 6D object pose estimation, proposing novel frameworks such as SGPA and cascaded relation networks (collectively exceeding 245 citations) that address intra-class variation for unseen object instances — a critical capability for real-world robotics and augmented reality. In surgical AI, she has pioneered methods for instrument segmentation using temporal motion priors, neural rendering for deformable tissue reconstruction, and dynamic surgical scene understanding, demonstrating a sustained commitment to making robotic surgery safer and more autonomous. Her open-source platform SurRoL (88 citations) has accelerated reinforcement learning research for surgical robots, while her human-in-the-loop embodied intelligence work pushes toward practical surgical automation. Her involvement in the HeiChole benchmark (96 citations) further reflects her leadership in standardizing surgical workflow analysis. Across her portfolio, Dou's research consistently translates rigorous algorithmic innovation into clinically meaningful and robotically deployable solutions.
Research Focus
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