Yaming Zhang
Papers
1
Total Citations
33
H-Index
1
About
Yaming Zhang is a researcher in computer vision and 3D geometric deep learning, with a focus on developing robust models for point cloud analysis. Their most cited work introduces a novel Graph Convolutional Network (GCN) architecture that achieves state-of-the-art classification performance while maintaining resilience to pose variations—a critical challenge in real-world 3D perception tasks. This 2021 paper, with 33 citations, demonstrates Zhang's ability to address fundamental limitations in point cloud processing by integrating spatial reasoning with graph-based learning. The work has been recognized for its practical implications in autonomous navigation, robotics, and augmented reality, where sensor data often contains rotational and translational noise. Zhang's contributions advance the field by bridging the gap between theoretical graph neural network design and applied 3D scene understanding, offering a scalable solution for pose-invariant feature extraction. Their research continues to influence subsequent work on robust point cloud representations, making Yaming Zhang a notable figure in the intersection of geometric deep learning and computer vision.
Research Focus
Key Achievements
Top Papers
- 1A novel GCN-based point cloud classification model robust to pose variances33 citations · 2021