Shujuan Huang
Papers
1
Total Citations
2
H-Index
1
About
Shujuan Huang is a computer vision researcher whose work focuses on advancing geometric perception for robotics and autonomous driving. Her key research areas include relative pose estimation, feature matching, and multi-modal sensor fusion. Huang’s most notable contribution is the development of **Str-L Pose**, a novel framework that integrates point features with structured line segments within a dual graph architecture to improve relative pose estimation. This approach addresses a critical limitation of conventional methods, which rely heavily on point matches and are vulnerable to mismatches, leading to degraded performance in challenging environments. By incorporating structured lines, Str-L Pose enhances robustness and accuracy in estimating camera motion. While her most-cited paper (2024) currently has 2 citations, it represents an emerging and promising direction in the field. Huang’s work is particularly relevant for applications requiring precise spatial understanding, such as autonomous navigation and 3D reconstruction. Her research contributes to making vision-based systems more reliable in real-world scenarios where traditional point-only methods fail.
Research Focus
Key Achievements
Top Papers
- 1