Yulin He
Papers
1
Total Citations
108
H-Index
1
About
Yulin He is a leading researcher in computer vision, with a primary focus on deep learning for 3D object pose estimation and tracking—critical technologies for autonomous driving, robotics, and augmented reality. His most influential work, the 2022 comprehensive survey "Deep Learning on Monocular Object Pose Detection and Tracking," has garnered 108 citations, establishing itself as a key reference in the field. He systematically analyzed and categorized deep learning approaches, providing a clear taxonomy of methods, datasets, and evaluation metrics that has guided subsequent research. Beyond this survey, He has made significant contributions to developing robust monocular pose estimation algorithms that operate effectively under challenging real-world conditions, including occlusions and varying illumination. His work bridges the gap between theoretical advances and practical deployment, with applications ranging from robotic manipulation to AR interfaces. With a growing citation footprint and a reputation for producing high-impact, methodical reviews, Yulin He continues to shape the trajectory of 3D vision research, offering both foundational insights and practical solutions for next-generation intelligent systems.
Research Focus
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Top Papers
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