Yanfeng Gu

Harbin Institute of Technology, Fudan University

Papers

Total Citations

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About

Yanfeng Gu is a researcher whose work bridges computer vision and robotics, with a focus on perception under challenging real-world conditions. His key research areas include robotic grasping, amodal segmentation, and optimization methods for computer vision tasks. Gu’s most notable contribution is the development of LAC-Net (Linear-Fusion Attention-Guided Convolutional Network), a novel architecture that addresses the critical problem of robotic grasping under occlusion. By enabling robots to infer complete object shapes even when partially hidden, this work advances the practical deployment of autonomous systems in cluttered environments. His earlier work on robust cost functions for optimizing chamfer masks demonstrates a sustained interest in foundational vision problems. While his citation counts are currently modest—with each of his most-cited papers garnering three citations—the timeliness of his research on occlusion handling suggests growing impact as robotic perception systems become increasingly important in manufacturing, logistics, and service robotics. Gu’s contributions represent meaningful steps toward more perceptually capable autonomous systems.