Hyunse Yoon

Papers

Total Citations

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About

Hyunse Yoon is a researcher at the intersection of computer vision and robotics, with a primary focus on camera calibration and geometric correspondence. His most notable contribution is the development of a deep learning-based approach for chessboard corner detection, a critical prerequisite for accurate camera calibration in applications ranging from augmented reality to 3D reconstruction and motion estimation. In his 2021 paper, Yoon introduced a multi-task learning framework that simultaneously detects corners and refines their sub-pixel locations, significantly improving robustness under challenging conditions like occlusion, blur, and non-uniform lighting. While his most cited work has garnered 4 citations—a modest count reflecting its niche but foundational role—the methodology has been recognized for its practical utility in real-world robotic and vision systems. Yoon’s work addresses a persistent bottleneck in calibration pipelines, offering a more reliable alternative to traditional corner detection algorithms. His research continues to influence the development of automated calibration tools, making him a valuable contributor to the fields of robotics and computer vision.