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Theses

Alignement de données 2D, 3D et applications en réalité augmentée.

Youssef El Rhabi 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : This thesis belongs within the context of augmented reality. The main issue resides in estimating a camera pose in real-time. This estimation should be done following three main criteria: precision, robustness and computation efficiency.In the frame of this thesis we established methods enabling better use of image primitives. As far as we are concerned, we limit ourselves to keypoint primitives. We first set an architecture enabling faster pose estimation without loss of precision or robustness. This architecture is based on using data collected during an offline phase. This offline phase is used to construct a 3D point cloud of the scene. We use those data in order to build a neighbourhood graph within the images in the database. This neighbourhood graph enables us to select the most relevant images in order to compute the camera pose more efficiently. Since the description and matching processes are not fast enough with SIFT descriptor, we decided to optimise the bottleneck parts of the whole pipeline. It led us to propose our own descriptor. Towards this aim, we built a framework encompassing most recent binary descriptors including a recent state-of-the-art one named BOLD. We pursue a similar goal to BOLD, namely to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure introduced in BOLD.In this thesis we introduce several methods used to estimate camera poses more efficiently. Our work has been distinguished by two publications (a national and an international one) as well as with a patent application.
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Youssef El Rhabi. Alignement de données 2D, 3D et applications en réalité augmentée.. Traitement du signal et de l'image [eess.SP]. Normandie Université; Universitat autònoma de Barcelona, 2017. Français. ⟨NNT : 2017NORMC212⟩. ⟨tel-01563734⟩

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