Papers
2
Total Citations
28
H-Index
2
About
Shibei Xue is a rising researcher in computer vision and robotics, with a focused expertise in six-degree-of-freedom (6D) object pose estimation. Their work addresses a critical bottleneck in deploying accurate pose estimation models on resource-constrained platforms, such as robotic arms and augmented reality devices. Xue’s most notable contribution is the development of **HRPose**, a real-time, high-resolution 6D pose estimation network that leverages knowledge distillation to achieve both speed and accuracy. This work, published in 2023, has already garnered 25 citations, reflecting its immediate impact on the field. Building on this, Xue introduced **Lite-HRPE** in 2024, a method specifically designed for resource-limited platforms, tackling the practical challenge of deploying sophisticated models on hardware with limited computational power. By bridging the gap between state-of-the-art accuracy and real-world hardware constraints, Xue’s research is paving the way for more accessible and efficient robotic perception systems. Their work is particularly relevant for students and researchers interested in efficient deep learning, model compression, and the practical deployment of computer vision algorithms in robotics and augmented reality.
Research Focus
Key Achievements
Top Papers
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