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About

Chul Min Yeum is a leading researcher at the intersection of computer vision, robotics, and infrastructure health monitoring, pioneering methods to transform how civil infrastructure is inspected and maintained. His work centers on enabling human–machine collaboration, with major contributions in mixed reality, unsupervised defect segmentation, and gaze-based human-robot interaction. Yeum’s 2022 paper on mixed reality for infrastructure inspections has garnered 52 citations, establishing a foundational framework for integrating human expertise with automated systems. He has advanced defect detection by developing unsupervised segmentation techniques that overcome the limitations of traditional supervised bounding box detectors, which often capture excessive background and fail under perspective transformation. His recent work on gaze-based human-robot interaction systems addresses the critical need for objective, repeatable inspections of bridges and other critical infrastructure, moving beyond qualitative visual assessments. Yeum’s 2025 paper on LiDAR-3DGS introduces a novel multimodal initialization method for 3D Gaussian splats, further pushing the boundaries of robotic inspection capabilities. Through these innovations, Yeum is shaping a future where infrastructure inspections are safer, more accurate, and seamlessly collaborative between humans and machines.