Méthodes de Reconstruction Denses pour la Vision Active

Méthodes de Reconstruction Denses pour la Vision Active

Dense Reconstruction Methods for Active Vision

Emmanuelle Clergue Thierry Viéville 

INRIA, Unité de Recherche de Sophia-Antipolis 2004, route des Lucioles 06902 Sophia-Antipolis Cedex

Corresponding Author Email: 
24 March 1994
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This paper aims to analyse how to introduce 3D information into an active vision system . In order to do so, we propose to realize a dense reconstruction from a monocular sequence in an active vision application . We first present existing algorithms for dense reconstruction. After having compared their drawbacks and advantages, we describe the chosen algorithm . Finally, we show the results obtained from synthetic images and images acquired when using an artificial robotic head.


Ce papier cherche à analyser comment introduire des données tridimensionnelles au sein d'un système de vision active. En effet, nous nous sommes proposés de réaliser une reconstruction dense 3D à partir de l'analyse d'une séquence monoculaire, dans un paradigme de vision active. Nous présentons tout d'abord les différents algorithmes déjà existants dans le domaine de la reconstruction dense, en faisant ressortir leurs avantages et leurs inconvénients . Nous décrivons, ensuite, l'algorithme choisi pour pallier à certains de ces inconvénients. Enfin, nous montrons quelques résultats à partir d'images synthétiques ou de vues réelles acquises par la tête artificielle.


Active vision, Fast 3D reconstruction, Region segmentation, Regularization

Mots clés

Vision active, Reconstruction dense en 3D rapide, Segmentation région, Régularisation

1. Introduction
2. Méthodes De Reconstruction Dense
3. Choix De La Méthode De Reconstruction Proposée
4. Description De La Segmentation Régions
5. Mise En Correspondance Des Régions
6. Affinage Et Régularisation De La Carte De Profondeurs
7. Résultats
8. Conclusion

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