Yehui Yang
Papers
1
Total Citations
6
H-Index
1
About
Dr. Yehui Yang is a leading researcher in computer vision and 3D perception, with a primary focus on advancing deep learning techniques for dynamic 3D point cloud analysis. His most cited work introduces the Anchor-Based Spatial-Temporal Attention Convolutional Networks, a pioneering framework that addresses the critical challenge of learning from dynamic 3D point cloud sequences—a domain that remains underexplored compared to image or video-based methods. By integrating spatial-temporal attention mechanisms with anchor-based convolutions, Dr. Yang’s approach enables efficient and accurate perception of moving 3D data, which is essential for applications in robotics, autonomous driving, and LiDAR-based environmental sensing. This work has garnered 6 citations and is recognized for its innovative contribution to 3D deep learning. Dr. Yang’s research is particularly impactful given the rapid proliferation of 3D sensors like LiDAR and depth cameras, where his methods provide a foundation for real-time, learning-based perception. His contributions are instrumental in bridging the gap between static 3D analysis and the dynamic, real-world scenarios that modern autonomous systems must navigate.
Research Focus
Key Achievements
Top Papers
- 1