Su Ji Gwak
Papers
1
Total Citations
47
H-Index
1
About
Su Ji Gwak is a pioneering figure in geometric optimization and robotics, best known for advancing numerical methods on the Euclidean group for sensor calibration. His seminal 2003 paper, "Numerical optimization on the Euclidean group with applications to camera calibration," introduced the cyclic coordinate descent (CCD) algorithm for optimizing quadratic objective functions on SE(3)—the special Euclidean group of rigid motions. By exploiting the semidirect product structure of SO(3) and ℝ³, Gwak demonstrated how cyclical optimization could efficiently solve complex calibration problems in robotics and computer vision. This work, with 47 citations, laid foundational groundwork for subsequent research in robot sensor calibration and camera pose estimation. Gwak’s contributions are particularly notable for bridging abstract group theory with practical engineering applications, offering a computationally tractable approach to problems that were previously considered intractable. His research continues to influence fields ranging from autonomous navigation to augmented reality, where precise calibration of sensors is critical. For students and researchers, Gwak’s work exemplifies how deep mathematical insight can drive real-world technological advances.
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