118 citations · 200 across the 7 of their papers we have counts for
5 papers · 1 filter
InfraParis: A multi-modal and multi-task autonomous driving dataset
Gianni Franchi, Marwane Hariat, Xuanlong Yu +3
Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes. Cons…
Learning structure-from-motion from motion
Clément Pinard, Laure Chevalley, Antoine Manzanera +1
This work is based on a questioning of the quality metrics used by deep neural networks performing depth prediction from a single image, and then of the usability of recently publi…
Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network
Clément Pinard, Laure Chevalley, Antoine Manzanera +1
Using a neural network architecture for depth map inference from monocular stabilized videos with application to UAV videos in rigid scenes, we propose a multi-range architecture f…
End-to-end depth from motion with stabilized monocular videos
Clément Pinard, Laure Chevalley, Antoine Manzanera +1
We propose a depth map inference system from monocular videos based on a novel dataset for navigation that mimics aerial footage from gimbal stabilized monocular camera in rigid sc…
Exploring to learn visual saliency: The RL-IAC approach
Celine Craye, Timothee Lesort, David Filliat +1
The problem of object localization and recognition on autonomous mobile robots is still an active topic. In this context, we tackle the problem of learning a model of visual salien…