Xianda Guo
Papers
1
Total Citations
6
H-Index
1
About
Xianda Guo is a computer vision researcher whose work centers on 3D scene understanding, depth estimation, and stereo matching — foundational technologies driving advances in autonomous driving and robotics. His most recognized contribution to date is **OpenStereo** (2023), a comprehensive benchmark and strong baseline framework for stereo matching that addresses a critical gap in the field: the lack of standardized, fair comparisons across competing architectures. By providing a unified evaluation platform, OpenStereo empowers researchers to identify the most suitable methods for practical deployment, accelerating progress across the broader community. Guo's research sits at the intersection of geometric deep learning and real-world perception systems, tackling problems where precise spatial reasoning is essential for machine decision-making. His focus on rigorous benchmarking reflects a commitment not only to developing novel algorithms but to improving the reproducibility and comparability of results — a increasingly valued quality in modern AI research. Though early in citation accumulation, his OpenStereo framework has already begun attracting attention from researchers in autonomous systems and 3D vision, signaling its potential to become a standard reference point in stereo matching research for years to come.
Research Focus
Key Achievements
Top Papers
- 1