1 code implementation • 24 Oct 2022 • Gaétan Landreau, Mohamed Tamaazousti
Novel-view synthesis (NVS) can be tackled through different approaches, depending on the general setting: a single source image to a short video sequence, exact or noisy camera pose information, 3D-based information such as point clouds etc.
no code implementations • 17 Oct 2022 • Gaëtan Landreau, Mohamed Tamaazousti
Image-based 3D reconstruction has increasingly stunning results over the past few years with the latest improvements in computer vision and graphics.
1 code implementation • 15 Oct 2022 • Adrian Bojko, Romain Dupont, Mohamed Tamaazousti, Hervé Le Borgne
Thus, we propose a novel SLAM that learns when masking objects improves its performance in dynamic scenarios.
no code implementations • 7 Oct 2022 • Riccardo Finotello, Daniel L'Hermite, Celine Quéré, Benjamin Rouge, Mohamed Tamaazousti, Jean-Baptiste Sirven
The procedure is an end-to-end pipeline including the process of synthetic data augmentation, the construction of a suitable robust, homoscedastic, deep learning model, and the validation of its predictions.
no code implementations • 23 Dec 2021 • Mohamed Ouerfelli, Mohamed Tamaazousti, Vincent Rivasseau
Various numerical simulations for $k=3$ in the conventionally considered range $n \leq 1000$ show that the experimental performances of SMPI improve drastically upon existent algorithms and becomes comparable to the theoretical optimal recovery.
no code implementations • 30 Nov 2021 • Riccardo Finotello, Mohamed Tamaazousti, Jean-Baptiste Sirven
Laser-induced breakdown spectroscopy is a preferred technique for fast and direct multi-elemental mapping of samples under ambient pressure, without any limitation on the targeted element.
no code implementations • 20 Aug 2021 • Alexandre Morgand, Mohamed Tamaazousti, Adrien Bartoli
Specularity prediction using our new model is tested against the most recent JOLIMAS version on both synthetic and real sequences with objects of various general shapes.
2 code implementations • 4 Aug 2021 • Harold Erbin, Riccardo Finotello, Robin Schneider, Mohamed Tamaazousti
We continue earlier efforts in computing the dimensions of tangent space cohomologies of Calabi-Yau manifolds using deep learning.
no code implementations • 1 Jan 2021 • Mohamed Ouerfelli, Mohamed Tamaazousti, Vincent Rivasseau
We consider the Principal Component Analysis (PCA) problem for tensors $T \in (\mathbb{R}^n)^{\otimes k}$ of large dimension $n$ and of arbitrary order $k\geq 3$.
no code implementations • 12 Nov 2020 • Adrian Bojko, Romain Dupont, Mohamed Tamaazousti, Hervé Le Borgne
Our dataset includes consensus inversions, i. e., situations where the SLAM uses more features on dynamic objects that on the static background.
no code implementations • ICML 2020 • Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain Couillet
This paper shows that deep learning (DL) representations of data produced by generative adversarial nets (GANs) are random vectors which fall within the class of so-called \textit{concentrated} random vectors.
no code implementations • ICLR 2019 • Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet
In this paper, we present a random matrix approach to recover sparse principal components from n p-dimensional vectors.
no code implementations • 4 Apr 2019 • John Lin, Mohamed El Amine Seddik, Mohamed Tamaazousti, Youssef Tamaazousti, Adrien Bartoli
We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single image by generating its diffuse component.
1 code implementation • 27 Feb 2019 • Mohamed El Amine Seddik, Mohamed Tamaazousti, John Lin
In this paper, we present a general framework named \textit{Generative Collaborative Networks} (GCN), where the idea consists in optimizing the \textit{generator} (the mapping of interest) in the feature space of a \textit{features extractor} network.
1 code implementation • 27 Dec 2017 • Youssef Tamaazousti, Hervé Le Borgne, Céline Hudelot, Mohamed El Amine Seddik, Mohamed Tamaazousti
We also propose a unified framework of the methods based on the diversifying of the training problem.