RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features

@article{Schwarz2015RGBDOR,
  title={RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features},
  author={Max Schwarz and Hannes Schulz and Sven Behnke},
  journal={2015 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2015},
  pages={1329-1335}
}
Object recognition and pose estimation from RGB-D images are important tasks for manipulation robots which can be learned from examples. Creating and annotating datasets for learning is expensive, however. We address this problem with transfer learning from deep convolutional neural networks (CNN) that are pre-trained for image categorization and provide a rich, semantically meaningful feature set. We incorporate depth information, which the CNN was not trained with, by rendering objects from a… CONTINUE READING
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