Shuang Ye

Papers

Total Citations

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About

Shuang Ye is a researcher in computer vision and robotics, with a primary focus on 3D object pose estimation and tracking. Their most-cited work, "Iterative optimization for frame-by-frame object pose tracking" (2017), introduces a robust method for real-time, sequential pose refinement, addressing key challenges in dynamic environments. This contribution is foundational for applications in augmented reality, autonomous navigation, and robotic manipulation, where precise object localization is critical. While their citation count is modest, the work demonstrates technical rigor and practical relevance, often cited in studies on iterative pose optimization and visual tracking. Ye’s research bridges the gap between theoretical optimization and real-world deployment, offering efficient solutions for continuous pose estimation. Their approach emphasizes stability and accuracy, making it a valuable reference for researchers developing lightweight tracking systems. As the field advances toward more adaptive and real-time systems, Ye’s contributions provide a solid stepping stone, particularly for those exploring iterative methods in constrained computational settings.