Chenye Guan

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About

Chenye Guan is a computer vision researcher whose work sits at the intersection of omnidirectional imaging and depth perception for autonomous robotic systems. His research addresses a critical challenge in modern robotics: while 360° cameras have become increasingly accessible and valuable for enhancing environmental perception through their wide field of view, corresponding omnidirectional depth sensors remain costly and difficult to deploy. To bridge this gap, Guan developed Omnidirectional Depth Extension Networks (ODE-CNN), a deep learning framework designed to synthesize or extend depth information for full 360° scenes — effectively enabling robots to perceive spatial depth across their entire surroundings without requiring expensive omnidirectional depth hardware. This contribution has garnered 28 citations since its 2020 publication, reflecting meaningful uptake within the robotics and computer vision communities. His work represents a practical and economically significant step toward more capable autonomous perception systems, making omnidirectional depth estimation more accessible for real-world deployment. Researchers working in autonomous navigation, SLAM, or sensor fusion will find Guan's contributions particularly relevant to the ongoing challenge of building robust, cost-effective environmental awareness in robotic platforms.