Embedding Geometry in Generative Models for Pose Estimation of Object Categories

@inproceedings{Fenzi2014EmbeddingGI,
  title={Embedding Geometry in Generative Models for Pose Estimation of Object Categories},
  author={Michele Fenzi and J{\"o}rn Ostermann},
  booktitle={BMVC},
  year={2014}
}
Pose estimation for object classes is central in many Computer Vision tasks. Many approaches have been proposed to estimate the pose of an unknown object from a given category, and those based on local features have shown to be very effective. While some use 3D information obtained through CAD models [4] or 3D reconstructions [2], others have shown that coupling feature regression and view labeling efficiently solves this task [1, 5]. However, they rely solely on the discriminative power of… CONTINUE READING
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