Yahao Shi
Papers
1
Total Citations
5
H-Index
1
About
Yahao Shi is a rising researcher in computer vision and 3D geometric understanding, with a particular focus on parsing dynamic and articulated objects from point cloud data. His most notable contribution is the development of P^3-Net, a pioneering framework that learns explicit point correspondence from raw, unordered point cloud sequences to infer part mobility in 3D objects. This work addresses the fundamental challenge of enabling intelligent agents to understand how objects move—such as doors opening or drawers sliding—directly from sensor data. Though early in his career, with his flagship paper accumulating 5 citations, Shi’s research is positioned at the intersection of 3D perception and robotics, offering a novel pathway for machines to interpret articulated structures without manual annotation. His approach stands out for its ability to handle real-world, noisy point cloud sequences, making it relevant for applications in autonomous manipulation and scene understanding. As the field of 3D vision increasingly demands models that capture object functionality, Shi’s work on part mobility parsing represents a meaningful step toward more interactive and intelligent visual systems.
Research Focus
Key Achievements
Top Papers
- 1