Qing Xia
Papers
1
Total Citations
24
H-Index
1
About
Qing Xia is a researcher specializing in 3D reconstruction, computer vision, and sensor-based measurement systems, with a particular focus on the challenging problem of scanning and digitizing transparent and optically complex materials. Their most notable work addresses a significant gap in modern depth-sensing technology: the inability of conventional RGB-D sensors to accurately capture transparent objects, which have profound implications for industrial measurement, robot navigation, and virtual reality applications. In their 2017 paper, "Fusing Depth and Silhouette for Scanning Transparent Object with RGB-D Sensor," Xia proposed an innovative approach that combines depth data with silhouette information to reconstruct surfaces that traditional range sensors cannot reliably capture — a contribution that has garnered 24 citations and demonstrated meaningful influence within the computer vision and 3D scanning communities. This work sits at the intersection of practical engineering challenges and fundamental perception problems, making it relevant to researchers working on robotic manipulation, augmented reality, and automated inspection systems. Xia's research reflects a commitment to solving real-world sensing limitations through creative data fusion strategies, positioning their work as a valuable reference point for those tackling non-Lambertian surface reconstruction challenges.
Research Focus
Key Achievements
Top Papers
- 1