KyeongKeun Baek
Papers
3
Total Citations
42
H-Index
2
About
KyeongKeun Baek is a researcher whose work lies at the intersection of computer vision, robotics, and factory automation, with a particular focus on bin-picking systems. His major contributions center on developing structured light-based methods that enable robots to perceive and manipulate randomly oriented parts from bins—a critical challenge in industrial automation. Baek’s most-cited paper, “Development of Structured Light Based Bin–Picking System Using Primitive Models” (2010), with 21 citations, demonstrates his approach to using geometric primitives for object pose estimation without requiring complete prior knowledge of the part. This work, alongside an earlier 2009 version, has influenced the design of flexible, vision-guided robotic systems. Baek also explored robot self-modeling for rotational symmetric objects, contributing to generic object category descriptions. While his citation counts are modest, his research addresses practical, real-world manufacturing problems, making his contributions valuable for engineers and researchers advancing automated pick-and-place technologies.
Research Focus
Key Achievements
Top Papers
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