Authors:
Niklas Gard
1
;
Anna Hilsmann
1
and
Peter Eisert
2
;
1
Affiliations:
1
Vision and Imaging Technologies, Fraunhofer HHI, Einsteinufer 37, 10587 Berlin, Germany
;
2
Institute for Computer Science, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany
Keyword(s):
6DoF Tracking, 6DoF Pose Estimation, Multi-object, Synthetic Training, Monocular, Augmented Reality.
Abstract:
In this paper, we present a multi-object 6D detection and tracking pipeline for potentially similar and non-textured objects. The combination of a convolutional neural network for object classification and rough pose estimation with a local pose refinement and an automatic mismatch detection enables direct application in real-time AR scenarios. A new network architecture, trained solely with synthetic images, allows simultaneous pose estimation of multiple objects with reduced GPU memory consumption and enhanced performance. In addition, the pose estimates are further improved by a local edge-based refinement step that explicitly exploits known object geometry information. For continuous movements, the sole use of local refinement reduces pose mismatches due to geometric ambiguities or occlusions. We showcase the entire tracking pipeline and demonstrate the benefits of the combined approach. Experiments on a challenging set of non-textured similar objects demonstrate the enhanced qua
lity compared to the baseline method. Finally, we illustrate how the system can be used in a real AR assistance application within the field of construction.
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