Hongbao Mo
Papers
1
Total Citations
4
H-Index
1
About
Hongbao Mo is a researcher specializing in computer vision and depth estimation, with a particular focus on fusing stereo vision with sparse depth measurements. His most notable contribution, the 2022 paper "Cross-based dense depth estimation by fusing stereo vision with measured sparse depth," has garnered 4 citations, reflecting its emerging impact in the field. This work addresses a critical challenge in autonomous systems and robotics: generating dense, accurate depth maps from limited sensor data. By integrating stereo vision with sparse depth inputs—such as those from LiDAR or structured light sensors—Mo's cross-based approach enhances depth estimation robustness in real-world scenarios, reducing computational overhead while maintaining precision. His research bridges the gap between traditional stereo algorithms and modern sensor fusion techniques, offering practical solutions for applications like autonomous driving and augmented reality. Mo's work is notable for its methodological clarity and potential to improve perception systems in resource-constrained environments. As a researcher, he continues to advance the intersection of computer vision and sensor integration, making his contributions valuable for students and engineers seeking efficient depth estimation methods.
Research Focus
Key Achievements
Top Papers
- 1