Robin Magnet
Postdoctoral Researcher · Inria Paris

I am currently a postdoc with Jean Feydy in the HeKA group at Inria Paris, where I work on shape and volume analysis with medical imaging, applied to bone fracture detection.

Before that, I obtained my PhD under the supervision of Maks Ovsjanikov at École Polytechnique (in the GeomeriX Team at LIX), studying scalable shape comparison using functional maps and spectral methods.

I am also the maintainer of pyFM, a Python library for functional maps computations.

News

Jul 2025 I gave a graduate school class at the SGP Summer School 2025 in Bilbao on Robust Methods for Non-Rigid Spectral Shape Matching. Replay available here.
2025 I presented my work at IABM 2025 in Nice.
Jan 2025 I released a Python implementation of Reversible Harmonic Maps — check it out here.
Oct 2024 I started a postdoc at Inria Paris in the HeKA Team.
Sep 2024 I defended my PhD thesis!
All news

Publications

PhD Thesis
Robust Spectral Methods for Shape Analysis and Deformation Assessment
Robin Magnet
Ph.D. dissertation, Institut Polytechnique de Paris, 2024
@phdthesis{magnet2024robust,
  title  = {Robust Spectral Methods for Shape Analysis
            and Deformation Assessment},
  author = {Magnet, Robin},
  school = {Institut Polytechnique de Paris},
  year   = {2024}
}
CVPR 2024
Scalable and Simplified Functional Map Learning
Robin Magnet, Maks Ovsjanikov
CVPR 2024 · IEEE/CVF Conference on Computer Vision and Pattern Recognition
@inproceedings{magnetMemoryScalableSimplifiedFunctional2024,
  title = {Memory-{{Scalable}} and {{Simplified Functional Map Learning}}},
  booktitle = {2024 {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  author = {Magnet, Robin and Ovsjanikov, Maks},
  year = 2024,
  publisher = {IEEE},
  doi = {10.1109/CVPR52733.2024.00387},
}
Journal of Morphology 2023
Assessing Craniofacial Growth and Form Without Landmarks: A New Automatic Approach Based on Spectral Methods
Robin Magnet, Kevin Bloch, Maxime Taverne, Simone Melzi, Maya Geoffroy, Roman H. Khonsari, Maks Ovsjanikov
Journal of Morphology, 2023
@article{magnet2023assessing,
  title   = {Assessing Craniofacial Growth and Form Without Landmarks:
             A New Automatic Approach Based on Spectral Methods},
  author  = {Magnet, Robin and Bloch, Kevin and Taverne, Maxime and
             Melzi, Simone and Geoffroy, Maya and Khonsari, Roman H.
             and Ovsjanikov, Maks},
  journal = {Journal of Morphology},
  year    = {2023}
}
Eurographics 2023
Scalable and Efficient Functional Map Computations on Dense Meshes
Robin Magnet, Maks Ovsjanikov
Eurographics 2023 · Oral presentation
@article{magnetScalableEfficientFunctional2023,
  title = {Scalable and {{Efficient Functional Map Computations}} on {{Dense Meshes}}},
  author = {Magnet, Robin and Ovsjanikov, Maks},
  year = 2023,
  journal = {Computer Graphics Forum},
  doi = {10.1111/cgf.14746}
}
3DV 2022
Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization
3DV 2022 · International Conference on 3D Vision · Oral presentation
🏆 Best Paper Award
@inproceedings{magnetSmoothNonRigidShape2022,
  title = {Smooth {{Non-Rigid Shape Matching}} via {{Effective Dirichlet Energy Optimization}}},
  booktitle = {2022 {{International Conference}} on {{3D Vision}} ({{3DV}})},
  author = {Magnet, Robin and Ren, Jing and {Sorkine-Hornung}, Olga and Ovsjanikov, Maks},
  year = 2022,
  publisher = {IEEE},
  doi = {10.1109/3DV57658.2022.00061},
}
ICCV 2021
DWKS: A Local Descriptor of Deformations Between Meshes and Point Clouds
Robin Magnet, Maks Ovsjanikov
ICCV 2021 · IEEE/CVF International Conference on Computer Vision
@inproceedings{magnetDWKSLocalDescriptor2021,
  title = {{{DWKS}} : {{A Local Descriptor}} of {{Deformations Between Meshes}} and {{Point Clouds}}},
  shorttitle = {{{DWKS}}},
  booktitle = {2021 {{IEEE}}/{{CVF International Conference}} on {{Computer Vision}} ({{ICCV}})},
  author = {Magnet, Robin and Ovsjanikov, Maks},
  year = 2021,
  doi = {10.1109/ICCV48922.2021.00377},
}

Code

Python bindings for functional maps related computations. Core library for shape correspondence.
A lightweight library for a memory-scalable unified representation of correspondences.
GPU-compatible Python implementation of Reversible Harmonic Maps between discrete surfaces.
Python bindings for Discrete Optimization and Smooth Discrete Optimization for functional maps.