9 citations · 19 across the 6 of their papers we have counts for
7 papers · 1 filter
Rebalancing gradient to improve self-supervised co-training of depth, odometry and optical flow predictions
Marwane Hariat, Antoine Manzanera, David Filliat
We present CoopNet, an approach that improves the cooperation of co-trained networks by dynamically adapting the apportionment of gradient, to ensure equitable learning progress. I…
A study of deep perceptual metrics for image quality assessment
Rémi Kazmierczak, Gianni Franchi, Nacim Belkhir +2
Several metrics exist to quantify the similarity between images, but they are inefficient when it comes to measure the similarity of highly distorted images. In this work, we propo…
Does it work outside this benchmark? Introducing the Rigid Depth Constructor tool, depth validation dataset construction in rigid scenes for the masses
Clément Pinard, Antoine Manzanera
We present a protocol to construct your own depth validation dataset for navigation. This protocol, called RDC for Rigid Depth Constructor, aims at being more accessible and cheape…
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…
Binary Distance Transform to Improve Feature Extraction
Mariane Barros Neiva, Antoine Manzanera, Odemir Martinez Bruno
To recognize textures many methods have been developed along the years. However, texture datasets may be hard to be classified due to artefacts such as a variety of scale, illumina…