Xiaomei Lei
Papers
2
Total Citations
9
H-Index
2
About
Xiaomei Lei is a rising researcher in computer vision, with a focused expertise in 6D object pose estimation for complex, real-world environments. Her work directly addresses critical challenges in enabling technologies for augmented reality (AR), virtual reality (VR), robotics, and autonomous driving. Lei’s primary contribution lies in developing robust neural network architectures that overcome the significant hurdles of low accuracy and poor real-time performance when estimating an object’s 3D position and orientation from cluttered or occluded scene images. Her most cited work, "A Robust CoS-PVNet Pose Estimation Network in Complex Scenarios," has already garnered 7 citations since its 2024 publication, signaling its immediate impact on the field. She further advanced this domain with "RFF-PoseNet: A 6D Object Pose Estimation Network Based on Robust Feature Fusion in Complex Scenes," which tackles the dual demands of precision and speed. By pioneering methods for robust feature fusion, Lei is helping to bridge the gap between theoretical pose estimation and its reliable deployment in dynamic, unpredictable settings, making her a key voice in the next generation of perception systems.
Research Focus
Key Achievements
Top Papers
- 1A Robust CoS-PVNet Pose Estimation Network in Complex Scenarios7 citations · 2024
- 2