Yohann PERRON
Yohann PERRON

PhD Student

Imagine
EFEO

About Me

I am a PhD student at Imagine(LIGM), ENPC and EFEO working under the supervision of Loïc Landrieu, Vladyslav Sydorov and Christophe Pottier.

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Interests
  • Artificial Intelligence
  • Computer Vision
  • Archeology
Education

  • PhD Computer Vision applied to archeology

    EFEO & ENPC

  • Master MVA

    Université Paris Saclay

  • Ingenieur Polytechnicien

    Ecole Polytechnique - Institut Polytechnique de Paris (IPP)

📚 My Research

I work on Computer Vision apply to Geospacial data and in particular with aerial Lidar imaging. My current project concists in automating the archeological mapping of ancient Khmer (in south east asia) temple and settlements from aerial Lidar using state-of-the-art Computer Vision techniques.

*Angkor Wat Temple : left lidar point cloud, center heightmap, right target mapping*
Angkor Wat Temple : left lidar point cloud, center heightmap, right target mapping

Featured Publications
Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era
Open-Canopy: Towards Very High Resolution Forest Monitoring
Recent Publications
Eric Bezzam, Yohann Perron, Martin Vetterli (2025). Towards Robust and Generalizable Lensless Imaging With Modular Learned Reconstruction. IEEE Transactions on Computational Imaging.
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Yohann Perron, Vladyslav Sydorov, Adam P. Wijker, Damian Evans, Christophe Pottier, Loic Landrieu (2024). Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era. NeurIPS24.
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Fajwel Fogel, Yohann Perron, Nikola Besic, Laurent Saint-André, Agnès Pellissier-Tanon, Martin Schwartz, Thomas Boudras, Ibrahim Fayad, Alexandre D'Aspremont, Loic Landrieu, Phillipe Ciais (2024). Open-Canopy: Towards Very High Resolution Forest Monitoring. CVPR25.
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Yohann Perron, Eric Bezzam, Martin Vetterli (2024). A Modular and Robust Physics-Based Approach for Lensless Image Reconstruction. ICIP.
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Niccolò Pancino, Yohann Perron, Pietro Bongini, Franco Scarselli (2022). Drug Side Effect Prediction with Deep Learning Molecular Embedding in a Graph-of-Graphs Domain. Mathematics.
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