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Hi! I am a researcher at IGN, the French National Geographical Institute, in the computer science department STRUDEL. The focus of my research is to exploit the structure of spatial/temporal data to improve the precision and speed of learning methods.

During my PhD I studied learning and optimization structured by large graphs (total variation, graphical models, etc). I now work on remote sensing application such as  3D point clouds and satellite hyperspectral imagery. I have also developed an interest for structured deep models, such as graph convolutions, recurrent neural network, and attention-based encoders.

See my academic_CV for more details (updated in Oct 2020).

NEW: Our paper presenting Torch-points3D havs been accepted as an oral at 3DV 2020. Congrats to the awesome dev team for their impressive work.

NEW: Our newest paper on satellite time series with Vivien Sainte-Fare Garnot has been acccepted at CVPR 2020 for an oral! Check the paper and the code. Comes with a new benchmark too.

Selected Articles

Leveraging Class Hierarchies with Metric-Guided Prototype Learning
Vivien Sainte Fare Garnot, Loic Landrieu, 2020 preprint

Torch-Points3D: A Modular Multi-Task Framework for Reproducible Deep Learning on 3D Point Clouds, Thomas Chaton, Nicolas Chaulet, Sofiane Horache, Loic Landrieu, 3DV2020 (oral) arxiv

Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention, Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano, Nesrine Chehata, CVPR2020 (oral). [arXiv]

Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning, Loic Landrieu, Mohamed Boussaha, CVPR2019. [arXiv]

Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation, Hugo Raguet and Loic Landrieu, ICML2018  [arXiv]

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs, Loic landrieu and Martin Simonovsky, CVPR2018. [arXiv]

Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions on General Weighted Graphs. Loic Landrieu, Guillaume Obozinski, SIAM Journal on Imaging Science, 2017 [siam][hal]

A Structured Regularization Framework for Spatially Smoothing Semantic Labelings of 3D Point Clouds. Loic Landrieu, Hugo Raguet , Bruno Vallet , Clément Mallet, Martin Weinmann, ISPRS Journal of Photogrammetry and Remote Sensing, 2017 [hal] [isprs]

Preconditonning of a Generalized Forward-Backward Splitting and Application to Optimization on Graphs. Hugo Raguet, Loic Landrieu, SIAM Journal on Imaging Science, 2015 [siam][arxiv]

STUDENTS:


List of current and past PhD students:
Stéphane Guinard (Univ. Laval)
Mohamed Boussaha (Gambi-M)
Vivien Sainte-Fare Garnot
Raphael Sulzer
Romain Loiseau
Damien Robert


List of current and past interns:
Stephane Guinard, Simon Bailly, Omar Lahbib, Joana Roussillon, Thomas Luo (Helix.Re), Anna Kondracka (Vermessung AVT), Lamiae El-Mendili, Julien Baconat, Ameur Zaibi, Cédric Baron.

I teach machine learning at master level at ENSG and ENPC.

I review for ICML, NIPS, ICCV, CVPR, ACCV, BMVC, IJCV, PAMI, ANRT, ISPRS etc…