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of 78
pro vyhledávání: '"P. Trouvé-Peloux"'
Autor:
Leroy, Rémy, Trouvé-Peloux, Pauline, Champagnat, Frédéric, Saux, Bertrand Le, Carvalho, Marcela
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 learning has recently provided us with excellent results for monocular dept
Externí odkaz:
http://arxiv.org/abs/2107.14498
Autor:
Carvalho, Marcela, Saux, Bertrand Le, Trouvé-Peloux, Pauline, Champagnat, Frédéric, Almansa, Andrés
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 framework for learning semantics and local height together. We show how t
Externí odkaz:
http://arxiv.org/abs/1911.07543
Autor:
Carvalho, Marcela, Ferrera, Maxime, Boulch, Alexandre, Moras, Julien, Saux, Bertrand Le, Trouvé-Peloux, Pauline
This paper is a technical report about our submission for the ECCV 2018 3DRMS Workshop Challenge on Semantic 3D Reconstruction \cite{Tylecek2018rms}. In this paper, we address 3D semantic reconstruction for autonomous navigation using co-learning of
Externí odkaz:
http://arxiv.org/abs/1911.01082
We present a new dataset, dedicated to the development of simultaneous localization and mapping methods for underwater vehicles navigating close to the seabed. The data sequences composing this dataset are recorded in three different environments: a
Externí odkaz:
http://arxiv.org/abs/1910.14532
Publikováno v:
IROS Workshop - New Horizons for Underwater Intervention Missions: from Current Technologies to Future Applications, Oct 2018, Madrid, Spain
This paper presents a new underwater dataset acquired from a visual-inertial-pressure acquisition system and meant to be used to benchmark visual odometry, visual SLAM and multi-sensors SLAM solutions. The dataset is publicly available and contains g
Externí odkaz:
http://arxiv.org/abs/1809.07076
Autor:
Carvalho, Marcela, Saux, Bertrand Le, Trouvé-Peloux, Pauline, Almansa, Andrés, Champagnat, Frédéric
Depth estimation is of critical interest for scene understanding and accurate 3D reconstruction. Most recent approaches in depth estimation with deep learning exploit geometrical structures of standard sharp images to predict corresponding depth maps
Externí odkaz:
http://arxiv.org/abs/1809.01567
Publikováno v:
Sensors, MDPI, 2019
In the context of robotic underwater operations, the visual degradations induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, most localization methods are based on expensive navigational senso
Externí odkaz:
http://arxiv.org/abs/1806.05842
Autor:
Alice Fontbonne, Pauline Trouvé-Peloux, Frédéric Champagnat, Gabriel Jobert, Guillaume Druart
Publikováno v:
Sensors, Vol 23, Iss 23, p 9462 (2023)
Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a
Externí odkaz:
https://doaj.org/article/7078eaee42c74bdebb55fa7b871e8014
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Akademický článek
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