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About

Daiyun Shen is a rising researcher in the field of surgical robotics and computer vision, with a focus on real-time 3D scene reconstruction and instrument pose estimation. Their work addresses critical challenges in autonomous surgical systems, particularly the need for accurate, markerless tracking of surgical tools. Shen’s most-cited paper, “Deform3DGS: Flexible Deformation for Fast Surgical Scene Reconstruction with Gaussian Splatting” (2024, 36 citations), introduces a novel approach that leverages Gaussian splatting for rapid, deformable reconstruction of surgical scenes—a key enabler for dynamic, real-time visualization during procedures. Additionally, their contribution to the “SurgRIPE challenge: Benchmark of surgical robot instrument pose estimation” (2025, 5 citations) provides a standardized benchmark for evaluating vision-based pose estimation methods, advancing the field toward markerless, autonomous surgical task execution. By combining speed, flexibility, and accuracy, Shen’s work is paving the way for next-generation robotic surgery systems that can perceive and adapt to their environment in real time.