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Willow and Challenge
3D Instance Segmentation
Common
|...
License: Unknown

Overview

The Willow dataset is composed of 24 multi-view sequences totalling 353 RGB-D frames. The number
of objects in the different sequences amounts to 110, resulting in 1628 object instances (some
of them totally occluded in some frames). The Challenge dataset is composed of 39 multi-view
sequences totalling 176 RGB-D frames. The number of objects in the different sequences amounts
to 97, resulting in 434 object instances.

Citation

@inproceedings{aldoma2014automation,
  title={Automation of "ground truth" annotation for multi-view RGB-D object instance recognition
datasets},
  author={Aldoma, Aitor and F{\"a}ulhammer, Thomas and Vincze, Markus},
  booktitle={Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International
Conference on},
  pages={5016--5023},
  year={2014},
  organization={IEEE}
}
@inproceedings{faeulhammer2015_featInt,
  title={Temporal Integration of Feature Correspondences For Enhanced Recognition
in Cluttered And Dynamic Environments},
  author={F{\"a}ulhammer, Thomas and Aldoma, Aitor and Zillich, Michael and Vincze, Markus},
  booktitle={Proc.\ of the International Conference on Robotics and Automation (ICRA)},
  year={2015},
  organization={IEEE}
}
@inproceedings{faeulhammer2015mva,
  title={Multi-View Hypotheses Transfer for Enhanced Object Recognition in Clutter},
  author={F{\"a}ulhammer, Thomas and Zillich, Michael and Vincze, Markus},
  booktitle={IAPR Conference on Machine Vision Applications (MVA)},
  year={2015}
}
Data Summary
Type
Point Cloud,
Amount
--
Size
4.08GB
Provided by
Vienna Univerisity of Technology
TU Wien is one of the major universities in Vienna, Austria. The university has received extensive international and domestic recognition in teaching as well as in research, and it is a highly esteemed partner of innovation-oriented enterprises.
| Amount -- | Size 4.08GB
Willow and Challenge
3D Instance Segmentation
Common
License: Unknown

Overview

The Willow dataset is composed of 24 multi-view sequences totalling 353 RGB-D frames. The number
of objects in the different sequences amounts to 110, resulting in 1628 object instances (some
of them totally occluded in some frames). The Challenge dataset is composed of 39 multi-view
sequences totalling 176 RGB-D frames. The number of objects in the different sequences amounts
to 97, resulting in 434 object instances.

Citation

@inproceedings{aldoma2014automation,
  title={Automation of "ground truth" annotation for multi-view RGB-D object instance recognition
datasets},
  author={Aldoma, Aitor and F{\"a}ulhammer, Thomas and Vincze, Markus},
  booktitle={Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International
Conference on},
  pages={5016--5023},
  year={2014},
  organization={IEEE}
}
@inproceedings{faeulhammer2015_featInt,
  title={Temporal Integration of Feature Correspondences For Enhanced Recognition
in Cluttered And Dynamic Environments},
  author={F{\"a}ulhammer, Thomas and Aldoma, Aitor and Zillich, Michael and Vincze, Markus},
  booktitle={Proc.\ of the International Conference on Robotics and Automation (ICRA)},
  year={2015},
  organization={IEEE}
}
@inproceedings{faeulhammer2015mva,
  title={Multi-View Hypotheses Transfer for Enhanced Object Recognition in Clutter},
  author={F{\"a}ulhammer, Thomas and Zillich, Michael and Vincze, Markus},
  booktitle={IAPR Conference on Machine Vision Applications (MVA)},
  year={2015}
}
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