Linfang Zheng

Southern University of Science and Technology

Papers

Total Citations

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About

Linfang Zheng is a robotics researcher whose work centers on 6D object pose tracking, a critical capability for enabling robots to reliably interact with their environments. Her key contributions address the challenge of tracking symmetric, textureless objects under occlusion—a notoriously difficult problem in computer vision and robotics. In her notable 2022 paper, "TP-AE: Temporally Primed 6D Object Pose Tracking with Auto-Encoders," Zheng introduces a novel approach that leverages temporal information and auto-encoder architectures to achieve fast and accurate pose tracking. This work, which has already garnered 12 citations, demonstrates her ability to combine efficiency with robustness, making it highly relevant for real-time robotic applications. By tackling the limitations of existing methods for textureless and occluded objects, Zheng's research advances the practical deployment of robotic systems in dynamic, unstructured settings. Her work is particularly impactful for students and researchers interested in the intersection of deep learning, temporal modeling, and robotic manipulation, offering a clear path toward more reliable and adaptive autonomous systems.