Libin Liu
Papers
2
Total Citations
5
H-Index
1
About
Libin Liu is a researcher at the forefront of efficient deep learning and edge computing, with a primary focus on 3D point cloud analytics and human pose analysis. In their highly cited work, "Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge," Liu addresses the critical challenge of deploying 3D object detection—essential for autonomous driving and robotics—on resource-constrained edge devices. By leveraging pre-trained 2D models, this work achieves near real-time performance with limited computation, garnering significant attention for its practical impact on real-world deployment. Liu also advances human-centric AI with "A Spatial-Temporal Transformer Based Framework for Human Pose Assessment and Correction in Education Scenarios," introducing a novel architecture that captures both spatial and temporal dynamics for accurate pose evaluation. This work has immediate applications in sports analysis, healthcare, and interactive learning environments. With a growing citation footprint, Liu’s contributions are shaping the future of efficient, real-time AI systems, bridging the gap between state-of-the-art models and the constraints of edge hardware.
Research Focus
Key Achievements
Top Papers
- 1
- 2