Papers
1
Total Citations
2
H-Index
1
About
Mo Wu is a researcher specializing in computer vision and robotics, with a focus on 6D pose estimation for industrial automation. Their key contributions lie in developing robust methods for multi-instance pose estimation from depth images and point cloud data, addressing critical challenges such as pseudo outliers, instance occlusions, and low model-to-instance overlap. Wu’s most cited work, "Robust multi-view PPF-based method for multi-instance pose estimation" (2025), introduces an innovative approach that leverages point pair features (PPF) across multiple views to enhance accuracy and reliability in complex, cluttered environments. This work has already garnered 2 citations, signaling its growing impact in the field. By tackling fundamental obstacles in robotic perception, Wu’s research directly advances the capabilities of industrial robots and automated systems, enabling more precise object manipulation and scene understanding. Their work is particularly notable for its practical applicability in real-world manufacturing settings, where robust pose estimation is essential for tasks like bin picking and assembly. As a rising voice in computer vision, Mo Wu continues to push the boundaries of what is possible in 3D perception for robotics.
Research Focus
Key Achievements
Top Papers
- 1Robust multi-view PPF-based method for multi-instance pose estimation2 citations · 2025