Catadioptric silhouette-based pose estimation from learned models

Authors:
Christian Reinbacher;Markus Heber;Matthias Rüther;Horst Bischof
Affiliations:
Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria
Venue:
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Year:
2011

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Abstract

The automated handling of objects requires the estimation of object position and rotation with respect to an actuator. We propose a system for silhouette-based pose estimation, which can be applied to a variety of objects, including untextured and slightly transparent objects. Pose estimation inevitably relies on previous knowledge of the object's 3D geometry. In contrast to traditional view-based approaches our system creates the required 3D model solely from the object silhouettes and abandons the need to obtain a model beforehand. It is sufficient to rotate the object in front of the catadioptric camera system. Experimental results show that the pose estimation accuracy drops only slightly compared to a highly accurate input model. The whole system utilizes the parallel processing power of graphics cards, to deliver an auto calibration in 20 s and reconstructions and pose estimations in 200ms.