4 citations · 5 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
BRDF-NeRF: Neural Radiance Fields with Optical Satellite Images and BRDF Modelling
Lulin Zhang, Ewelina Rupnik, Tri Dung Nguyen +2
Neural radiance fields (NeRF) have gained prominence as a machine learning technique for representing 3D scenes and estimating the bidirectional reflectance distribution function (…
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…
SparseSat-NeRF: Dense Depth Supervised Neural Radiance Fields for Sparse Satellite Images
Lulin Zhang, Ewelina Rupnik
Digital surface model generation using traditional multi-view stereo matching (MVS) performs poorly over non-Lambertian surfaces, with asynchronous acquisitions, or at discontinuit…