Papers
3
Total Citations
67
H-Index
3
About
Yi Shan is a researcher whose work spans the intersection of computer vision, autonomous systems, and intelligent robotics — fields increasingly central to modern technological advancement. Shan's most influential contribution lies in hardware-accelerated stereo vision, with a 2014 paper on Mini-Census Adaptive Support Region garnering 38 citations, demonstrating early pioneering work in enabling accurate depth estimation for autonomous vehicles, robotics, and aerial survey applications. This research addressed the demanding computational challenges of real-time 3D scene reconstruction — a foundational problem in embedded vision systems. Building on this foundation, Shan expanded into semantic segmentation for autonomous driving, with a 2022 paper on Cross-Dataset Collaborative Learning accumulating 26 citations. This work tackled a critical limitation in the field — the difficulty of generalizing segmentation models across diverse datasets — proposing collaborative learning strategies to improve scene understanding in real-world driving conditions. More recently, Shan has extended expertise into surgical robotics, contributing to the development of an intelligent human-machine collaborative craniotomy system integrating a UR5 robotic arm with multi-axis force sensing. Together, these contributions reflect a researcher adept at bridging perception algorithms, hardware implementation, and intelligent system design across impactful domains.
Research Focus
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Top Papers
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