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Camera Pose Estimation with Semantic 3D Model

Vincent Gaudillière 1 Gilles Simon 1 Marie-Odile Berger 1
1 MAGRIT-POST - Augmentation visuelle d'environnements complexes
Inria Nancy - Grand Est, LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
Abstract : In computer vision, estimating camera pose from correspondences between 3D geometric entities and their projections into the image is a widely investigated problem. Although most state-of-the-art methods exploit simple primitives such as points or lines, and thus require dense scene models, the emergence of very effective CNN-based object detectors in the recent years has paved the way to the use of much lighter 3D models composed solely of a few semantically relevant features. In that context, we propose a novel model-based camera pose estimation method in which the scene is modeled by a set of virtual ellipsoids. We show that 6-DoF camera pose can be determined by optimizing only the three orientation parameters, and that at least two correspondences between 3D ellipsoids and their 2D projections are necessary in practice. We validate the approach on both simulated and real environments.
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Submitted on : Friday, July 12, 2019 - 2:58:35 PM
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Vincent Gaudillière, Gilles Simon, Marie-Odile Berger. Camera Pose Estimation with Semantic 3D Model. IROS 2019 - 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nov 2019, Macau, Macau SAR China. ⟨10.1109/IROS40897.2019.8968180⟩. ⟨hal-02181962⟩



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