Willow and Challenge
3D Instance Segmentation
Common
|...
许可协议: 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}
}
数据概要
数据格式
Point Cloud,
数据量
--
文件大小
4.08GB
发布方
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.
数据集反馈
| 47 | 数据量 -- | 大小 4.08GB
Willow and Challenge
3D Instance Segmentation
Common
许可协议: 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}
}
数据集反馈
0
立即开始构建AI
graviti
wechat-QR
长按保存识别二维码,关注Graviti公众号