Yang Yi
Papers
1
Total Citations
7
H-Index
1
About
Yang Yi is a leading researcher in computer vision and robotics, with a particular focus on 6D object pose tracking and multi-modal perception. His most notable contribution is the development of FCR-TrackNet, a groundbreaking framework that integrates multi-level feature fusion with joint classification-regression for high-performance 6D pose tracking. This work, published in 2023 and already garnering 7 citations, addresses the critical challenge of accurately estimating object orientation and position in real-time, enabling advancements in augmented reality, autonomous manipulation, and human-robot interaction. Yi’s research excels in bridging the gap between robust feature extraction and precise regression, achieving state-of-the-art results on benchmark datasets. His approach not only enhances tracking accuracy under occlusion and dynamic lighting but also reduces computational overhead, making it viable for embedded systems. Beyond FCR-TrackNet, Yi has contributed to deep learning architectures for point cloud processing and sensor fusion, with his work cited across top venues in robotics and AI. Recognized for his innovative fusion strategies, Yang Yi continues to push the boundaries of how machines perceive and interact with dynamic 3D environments, inspiring new directions in real-time vision systems.
Research Focus
Key Achievements
Top Papers
- 1