Beilei Cui
Papers
6
Total Citations
125
H-Index
4
About
Beilei Cui is an emerging researcher at the forefront of surgical computer vision, with a particular focus on depth estimation, 3D scene reconstruction, and simultaneous localization and mapping (SLAM) within minimally invasive and robotic surgery. Her work addresses one of the most challenging problems in image-guided surgery: extracting reliable spatial information from monocular endoscopic video, where traditional depth cues are inherently limited. Cui's most influential contribution, "Surgical-DINO" (2024, 58 citations), demonstrates her innovative approach of adapting large-scale foundation models like DINOv2 for the specialized domain of surgical depth estimation, bridging the gap between general-purpose AI and clinical applications. Her pioneering work on "Endo-4DGS" (45 citations) further showcases her expertise in applying Gaussian Splatting techniques for dynamic endoscopic scene reconstruction, offering significant improvements over NeRF-based methods. She has also extended these capabilities to wireless capsule endoscopy and advanced Gaussian Splatting-driven SLAM systems, broadening the clinical scope of her research. With over 120 cumulative citations across publications spanning 2024–2025 alone, Cui is rapidly establishing herself as a significant voice in surgical AI, with her research holding clear implications for augmented reality visualization, surgical navigation, and robot-assisted interventions.
Research Focus
Key Achievements
Top Papers
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