Katia Genovese

Papers

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About

Katia Genovese is a leading researcher in computer vision and robotic manipulation, with a focus on autonomous assembly and precision calibration. Her work addresses critical challenges in real-world automation, particularly where uncertainty and tight tolerances are involved. Her most-cited paper, "Peg-in-Hole Using 3D Workpiece Reconstruction and CNN-based Hole Detection" (2020, 31 citations), introduces a deep learning approach for detecting holes in 3D-reconstructed workpieces, enabling robots to perform peg-in-hole tasks without prior knowledge of the target’s position. This work bridges computer vision and robotics, offering a robust solution for industrial assembly. More recently, her 2024 paper on "Single-image camera calibration with model-free distortion correction" (15 citations) presents a novel method that uses a single image of a planar speckle pattern to extract full camera calibration parameters, eliminating the need for complex multi-image setups. This innovation simplifies calibration for high-precision applications. Genovese’s contributions are notable for their practical impact, combining deep learning with geometric modeling to advance autonomous systems. Her work is widely cited by researchers in robotics, manufacturing, and computer vision, reflecting its significance in enabling more adaptive and accurate robotic operations.