Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories

@article{Fenzi2013ClassGM,
  title={Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories},
  author={Michele Fenzi and Laura Leal-Taix{\'e} and Bodo Rosenhahn and J{\"o}rn Ostermann},
  journal={2013 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2013},
  pages={755-762}
}
In this paper, we propose a method for learning a class representation that can return a continuous value for the pose of an unknown class instance using only 2D data and weak 3D labeling information. Our method is based on generative feature models, i.e., regression functions learned from local descriptors of the same patch collected under different viewpoints. The individual generative models are then clustered in order to create class generative models which form the class representation. At… CONTINUE READING
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