34 citations · 41 across the 6 of their papers we have counts for
6 papers
Multi-view dense image matching with similarity learning and geometry priors
Mohamed Ali Chebbi, Ewelina Rupnik, Paul Lopes +1
We introduce MV-DeepSimNets, a comprehensive suite of deep neural networks designed for multi-view similarity learning, leveraging epipolar geometry for training. Our approach inco…
An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset
Teng Wu, Bruno Vallet, Marc Pierrot-Deseilligny +1
Dense matching is crucial for 3D scene reconstruction since it enables the recovery of scene 3D geometry from image acquisition. Deep Learning (DL)-based methods have shown effecti…
DeepSim-Nets: Deep Similarity Networks for Stereo Image Matching
Mohamed Ali Chebbi, Ewelina Rupnik, Marc Pierrot-Deseilligny +1
We present three multi-scale similarity learning architectures, or DeepSim networks. These models learn pixel-level matching with a contrastive loss and are agnostic to the geometr…
Pointless Global Bundle Adjustment With Relative Motions Hessians
Ewelina Rupnik, Marc Pierrot-Deseilligny
Bundle adjustment (BA) is the standard way to optimise camera poses and to produce sparse representations of a scene. However, as the number of camera poses and features grows, ref…
Feature matching for multi-epoch historical aerial images
Lulin Zhang, Ewelina Rupnik, Marc Pierrot-Deseilligny
Historical imagery is characterized by high spatial resolution and stereo-scopic acquisitions, providing a valuable resource for recovering 3D land-cover information. Accurate geo-…
3D Building Model Fitting Using A New Kinetic Framework
Mathieu Brédif, Dider Boldo, Marc Pierrot-Deseilligny +1
We describe a new approach to fit the polyhedron describing a 3D building model to the point cloud of a Digital Elevation Model (DEM). We introduce a new kinetic framework that hid…