Xingzheng Lu
Papers
1
Total Citations
6
H-Index
1
About
Xingzheng Lu is a researcher specializing in computer vision and 3D reconstruction, with a particular focus on applying structure-from-motion (SfM) techniques to challenging, confined environments. His most cited work, "Monocular Video Frame Optimization Through Feature-Based Parallax Analysis for 3D Pipe Reconstruction" (2022, 6 citations), addresses a critical bottleneck in 3D reconstruction: the selection of geometrically optimal frames from monocular video for narrow spaces like drainage pipes. By introducing a feature-based parallax analysis method, Lu’s contribution enables more accurate and robust 3D modeling in environments where traditional SfM approaches struggle due to limited viewpoints and poor texture. This work has direct implications for infrastructure inspection, robotics, and civil engineering, offering a practical solution for automating pipe condition assessment. While his citation count is still growing, Lu’s research demonstrates a clear focus on solving real-world engineering problems through innovative computer vision techniques. His work represents an important step toward making 3D reconstruction more reliable in non-ideal, constrained settings—a niche with significant practical value for maintenance and safety applications.
Research Focus
Key Achievements
Top Papers
- 1