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

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 Volume 132, October 2017, Pages 102-118 [hal] [isprs]

Comparison of Belief Propagation and Graph-Cut Approaches for Conextual Classification of 3D LiDAR Point Cloud Data. Loic Landrieu, Clément Mallet, Martin Weinmann, IGARSS2017 [hal]

Weakly Supervised Segmentation-Aided Classification of Urban Scenes from 3D LiDAR Point Clouds. Stéphane Guinard, Loic Landrieu.  ISPRS Archive, 2017. [hal]

Cut pursuit: Fast Algorithms to Learn Piecewise Cconstant Functions on General Weighted Graphs. Loic landrieu, Guillaume Obozinski, SIAM Journal on Imaging Science 2017, Vol. 10, No. 4 : pp. 1724-1766 [siam] [hal]

Pré-segmentation pour la Classification Faiblement Supervisée de Scènes Urbaines à partir de Nuages de Points 3D LiDAR. ORASIS, Jun 2017 [hal]


Learning Structured Models on Weighted Graphs, with Application to Spatial Data Analysis. Loic landrieu, PhD Thesis [hal]

Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions on General Weighted Graphs. Loic landrieu, Guillaume Obozinski, AIStats2016, Cadiz, Spain   [jmlr] [hal]


Preconditonning of a Generalized Forward-BAckward Splitting and Application to Optimization on Graphs. Hugo Raguet, Loic Landrieu, SIAM Journal on Imaging Science 8, 4(2706-2739)    [siam] [arxiv]


Continuously Indexed Potts Models on Unoriented Graphs. Loic landrieu, Guillaume Obozinsk, UAI 2014 – Quebec, Canada, [auai]  [hal]