Yiru Niu
Papers
1
Total Citations
6
H-Index
1
About
Yiru Niu is a researcher whose work lies at the intersection of computer vision, 3D reconstruction, and infrastructure inspection. Her primary research focus is on developing robust structure-from-motion (SfM) techniques for challenging environments, particularly the narrow, confined spaces found in drainage pipes and other underground infrastructure. Niu’s most notable contribution is her work on monocular video frame optimization through feature-based parallax analysis, which directly addresses the critical problem of selecting geometrically optimal frames for 3D reconstruction in visually degraded settings. This approach, detailed in her highly cited 2022 paper, has garnered 6 citations and represents a significant step forward in automating the inspection and modeling of hard-to-reach infrastructure. By tackling the unique challenges of feature-poor, low-texture pipe interiors, Niu’s research bridges the gap between theoretical SfM methods and practical field applications, offering a pathway to more efficient, non-destructive evaluation of critical urban systems. Her work is especially relevant for civil engineers and computer vision researchers seeking to automate the 3D mapping of confined spaces.
Research Focus
Key Achievements
Top Papers
- 1