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About

Hyunjun Jung is a leading researcher in 3D computer vision and robotic manipulation, with a focus on category-level 6D object pose estimation and autonomous grasping. His most impactful work includes the creation of PhoCaL (45 citations), a multi-modal dataset designed to address photometrically challenging objects for category-level pose estimation—a critical step for real-world robotics and augmented reality. Jung further advanced the field with MonoGraspNet (42 citations), a pioneering method that achieves 6-DoF robotic grasping using only a single RGB image, overcoming the limitations of depth-dependent approaches on difficult surfaces. His large-scale HouseCat6D dataset (26+ citations) provides unprecedented annotation quality and pose variety for household objects, setting a new benchmark for category-level perception. Jung also contributed to the Robothon 2021 Grand Challenge, developing a robotic framework for autonomous assembly that combines precise positioning with tactile interaction. With over 100 total citations and a clear trajectory toward bridging perception and action, Jung’s work is essential reading for anyone interested in robust, real-world robotic vision and manipulation.