65 citations · 132 across the 7 of their papers we have counts for
7 papers
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport
Rémy Leroy, Pauline Trouvé-Peloux, Frédéric Champagnat +2
Good quality reconstruction and comprehension of a scene rely on 3D estimation methods. The 3D information was usually obtained from images by stereo-photogrammetry, but deep learn…
Translation-invariant interpolation of parametric dictionaries
Frédéric Champagnat, Cédric Herzet
In this communication, we address the problem of approximating the atoms of a parametric dictionary, commonly encountered in the context of sparse representations in "continuous" d…
Multi-Task Learning of Height and Semantics from Aerial Images
Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux +2
Aerial or satellite imagery is a great source for land surface analysis, which might yield land use maps or elevation models. In this investigation, we present a neural network fra…
Double frame Tomographic PTV at high seeding densities
Philippe Cornic, Benjamin Leclaire, Frédéric Champagnat +4
A novel method performing 3D PTV from double frame multi-camera images is introduced. Particle velocities are estimated by following three steps. Firstly, separate particle reconst…
Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?
Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux +2
Depth estimation is of critical interest for scene understanding and accurate 3D reconstruction. Most recent approaches in depth estimation with deep learning exploit geometrical s…
Point target detection and subpixel position estimation in optical imagery
Vincent Samson, Frédéric Champagnat, Jean-François Giovannelli
This paper addresses the issue of detecting point objects in a clutter background and estimating their position by image processing. We are interested in the specific context where…