• DOI:10.1109/tmech.2020.3048433
Corpus ID: 219878517

OrthographicNet: A deep transfer learning approach for 3D object recognition in open-ended domains

@article{Kasaei2019OrthographicNetAD,
  title={OrthographicNet: A deep transfer learning approach for 3D object recognition in open-ended domains},
  author={H. Kasaei},
  journal={arXiv: Robotics},
  year={2019}
}

• H. Kasaei
• Published 2019
• Computer Science, Engineering
• arXiv: Robotics

Service robots are expected to be more autonomous and efficiently work in human-centric environments. For this type of robots, open-ended object recognition is a challenging task due to the high demand for two essential capabilities:(i) the accurate and real-time response, and (ii) the ability to learn new object categories from very few examples on-site. These capabilities are required for such robots since no matter how extensive the training data used for batch learning, the robot might be… Expand

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