Papers
1
Total Citations
9
H-Index
1
About
Mingfei Wu is a researcher in computer vision and robotics, with a primary focus on stereo matching for autonomous navigation. His most-cited work, "Multi-Scale Cost Volumes Cascade Network for Stereo Matching" (2021, 9 citations), addresses a critical bottleneck in the field: the trade-off between accuracy and computational efficiency. Wu’s key contribution lies in proposing a cascade network architecture that leverages multi-scale cost volumes, enabling high-precision depth estimation without the prohibitive runtime typical of deep learning methods. This innovation directly improves robot perception systems, making real-time navigation more reliable. By systematically balancing speed and accuracy, Wu’s work has influenced subsequent research on efficient stereo algorithms, as reflected in its citation impact. His research underscores a practical, application-driven approach to solving real-world challenges in robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Multi-Scale Cost Volumes Cascade Network for Stereo Matching9 citations · 2021