Jean-Philippe Pernot

École nationale supérieure d'arts et métiers, Laboratoire d’Ingénierie des Systèmes Physiques et Numériques

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About

Jean-Philippe Pernot is a leading researcher at the intersection of geometric modeling, reverse engineering, and digital twin technologies. His work focuses on bridging the physical and digital worlds, with major contributions in deep learning for CAD model reconstruction from point clouds and the development of data-driven digital twins for Industry 4.0 applications. His most cited paper, "SMA-Net: Deep learning-based identification and fitting of CAD models from point clouds" (2022, 8 citations), introduces a novel neural network architecture that automates the extraction and fitting of CAD models from 3D scan data, significantly advancing reverse engineering processes. Pernot also explores quality metrics for structured light-based point cloud acquisitions (2022, 2 citations), ensuring accurate real-to-virtual transfers. His recent work on "A Data Structure for Developing Data-Driven Digital Twins" (2024, 2 citations) lays foundational frameworks for high-fidelity digital representations of physical assets, enabling advanced simulations and lifecycle optimization. Through these contributions, Pernot is shaping how complex systems are designed, monitored, and maintained in the era of smart manufacturing.