0000000000709077
AUTHOR
Nathan Crombez
Subsequent Keyframe Generation for Visual Servoing
International audience; In this paper, we study the problem of autonomous and reliable positioning of a camera w.r.t. an object when only this latter is known but not the rest of the scene. We propose to combine the advantages and efficiency of a visual servoing scheme and the generalization ability of a generative adversarial network. The paper describes how to efficiently create a synthetic dataset in order to train a network that predicts an intermediate visual keyframe between two images. Subsequent predictions are used as visual features to autonomously converge towards the desired pose even for large displacements. We show that the proposed method can be used without any prior knowled…
Multimodal 2D Image to 3D Model Registration via a Mutual Alignment of Sparse and Dense Visual Features
International audience; Many fields of application could benefit from an accurate registration of measurements of different modalities over a known 3D model. However, aligning a 2D image to a 3D model is a challenging task and is even more complex when the two have a different modality. Most of the 2D/3D registration methods are based on either geometric or dense visual features. Both have their own advantages and their own drawbacks. We propose, in this paper, to mutually exploit the advantages of one feature type to reduce the drawbacks of the other one. For this, an hybrid registration framework has been designed to mutually align geometrical and dense visual features in order to obtain …
Mélange de Gaussiennes Photométriques pour l'Asservissement Visuel Virtuel Direct d'une Caméra Omnidirectionnelle
International audience; Cet article traite du suivi de pose direct basé modèle 3D. Nous considérons la transformation d’images omnidirectionnelles en Mélange de Gaussiennes Photométriquesn (MGP) comme primitives directes. Les contributions sont d’adapter l’optimisation de pose aux caméras omnidirectionnelles et de repenser les règles d’initialisation et d’optimisation du paramètre d’extension du MGP. Plusieurs évaluations montrent que cette approche augmente la taille du domaine de convergence. L’application à des images acquises avec un robot mobile placé dans un environnement urbain, représenté par un grand nuage de points 3D coloré, montre une robustesse significative aux grands mouvemen…
Reliable Planar Object Pose Estimation in Light Fields From Best Subaperture Camera Pairs
International audience; A light-field camera can obtain richer information about a scene than a usual camera. This property offers a lot of potential for robot vision. In this paper, we present a method for pose estimation of a planar object with a light-field camera. The light-field camera can be regarded as a set of sub-aperture cameras. Although any combination of them can theoretically be used for the pose estimation, the accuracy depends on the combination. We show that the estimated pose error can be reduced by selecting the best pair of sub-aperture cameras. We have evaluated the accuracy of our approach with real experiments using a light-field camera in front of planar targets held…
Photometric Gaussian Mixtures for Direct Virtual Visual Servoing of Omnidirectional Camera
International audience
Outdoor Scenes Pixel-wise Semantic Segmentation using Polarimetry and Fully Convolutional Network
International audience; In this paper, we propose a novel method for pixel-wise scene segmentation application using polarimetry. To address the difficulty of detecting highly reflective areas such as water and windows, we use the angle and degree of polarization of these areas, obtained by processing images from a polarimetric camera. A deep learning framework, based on encoder-decoder architecture, is used for the segmentation of regions of interest. Different methods of augmentation have been developed to obtain a sufficient amount of data, while preserving the physical properties of the polarimetric images. Moreover, we introduce a new dataset comprising both RGB and polarimetric images…