Zhihua Xu
Papers
1
Total Citations
6
H-Index
1
About
Zhihua Xu is a researcher specializing in computer vision and 3D reconstruction, with a particular focus on infrastructure inspection and geometric optimization. His key research areas include structure-from-motion (SfM), monocular video analysis, and feature-based parallax methods for reconstructing complex, confined environments. Xu’s major contribution lies in developing a novel framework for optimizing monocular video frames through feature-based parallax analysis, specifically tailored for 3D pipe reconstruction—a challenging domain where traditional SfM techniques often fail due to narrow, texture-poor spaces. His most cited work, published in 2022, has garnered 6 citations and addresses the critical problem of selecting geometrically optimal frames from sequential video, enabling more accurate and efficient 3D modeling of drainage pipes. This work is notable for its practical application in civil infrastructure, offering a cost-effective solution for inspecting and mapping underground systems. Xu’s research bridges the gap between theoretical computer vision and real-world engineering challenges, making his contributions valuable for both academic researchers and industry practitioners working on automated inspection and digital twin technologies.
Research Focus
Key Achievements
Top Papers
- 1