Learning an object class representation on a continuous viewsphere

@article{Schels2012LearningAO,
  title={Learning an object class representation on a continuous viewsphere},
  author={Johannes Schels and Joerg Liebelt and Rainer Lienhart},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2012},
  pages={3170-3177}
}
We propose an approach to multi-view object class detection and approximate 3D pose estimation. It relies on CAD models as positive training examples and discriminatively learns photometric object parts such that an optimal coverage of intra-class and viewpoint variation is guaranteed. In contrast to previous work, the approach shows a significantly reduced training set dependency while avoiding any manual training supervision or annotation, since it is capable of deriving all relevant… CONTINUE READING
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