Nick Theisen

University of Koblenz and Landau, Koblenz University of Applied Sciences, Universität Koblenz

Papers

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About

Nick Theisen is a researcher advancing the frontiers of computer vision and robotics, with a focus on human-robot interaction and 3D perception. His most impactful contribution, "Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition" (2022, 43 citations), addresses a critical challenge in robotics: enabling machines to recognize novel human actions from just a single example. By formulating one-shot action recognition as a deep metric learning problem on skeletal data, Theisen’s work paves the way for robots to adapt to previously unseen human behavior in real time, a key enabler for intuitive and safe human-robot collaboration. In parallel, Theisen tackles the practical challenges of sensor fusion with his work on "Online Calibration of Extrinsic Parameters for Solid-State LIDAR Systems" (2024), a study that addresses the calibration of diverse solid-state LIDAR sensors with distinct scanning patterns. Earlier, his research on "Boosting 3D Shape Classification with Global Verification and Redundancy-free Codebooks" (2019) explored efficient 3D data representation by eliminating redundant information from codebooks. Through these contributions, Theisen demonstrates a commitment to making robotic systems more perceptive, adaptive, and reliable in dynamic, real-world environments.