Pei-Jung Liang
Papers
1
Total Citations
16
H-Index
1
About
Pei-Jung Liang is a researcher focused on advancing computer vision for autonomous systems, with a particular emphasis on 3D object detection using cost-effective monocular imaging. Her major contribution lies in developing a proposal generation network that enables accurate 3D object detection from single-camera inputs—a challenging task traditionally reliant on expensive LiDAR or stereo setups. This work, published in 2021, has garnered 16 citations, reflecting its relevance to the autonomous vehicle and robotics communities. By addressing the trade-off between cost and performance, Liang’s research helps make 3D perception more accessible for real-world deployment. Her approach serves as a practical auxiliary module for autonomous driving systems, demonstrating that monocular solutions can achieve meaningful detection accuracy without the hardware overhead of depth sensors. Liang’s work is notable for pushing the boundaries of monocular vision, offering a scalable path toward safer and more affordable autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1