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  1. LICENSE +19 -0
  2. README +44 -0
  3. cameras.zip +3 -0
  4. data/0003.zip +3 -0
  5. data/0004.zip +3 -0
  6. data/0006.zip +3 -0
  7. data/0008.zip +3 -0
  8. data/0011.zip +3 -0
  9. data/0014.zip +3 -0
  10. data/0017.zip +3 -0
  11. data/0019.zip +3 -0
  12. data/0023.zip +3 -0
  13. data/0024.zip +3 -0
  14. data/0026.zip +3 -0
  15. data/0027.zip +3 -0
  16. data/0028.zip +3 -0
  17. data/0029.zip +3 -0
  18. data/0034.zip +3 -0
  19. data/0038_part1.zip +3 -0
  20. data/0038_part2.zip +3 -0
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  43. data/0086.zip +3 -0
  44. data/0087.zip +3 -0
  45. data/0091.zip +3 -0
  46. image_sets.zip +3 -0
  47. keyframes.zip +3 -0
  48. models.zip +3 -0
  49. pairs.zip +3 -0
  50. poses.zip +3 -0
LICENSE ADDED
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+ Copyright (c) 2017 Robotics and State Estimation Lab at The University of Washington
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+
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+ Permission is hereby granted, free of charge, to any person obtaining a copy
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+ of this software and associated documentation files (the "Software"), to deal
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+ in the Software without restriction, including without limitation the rights
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+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+ copies of the Software, and to permit persons to whom the Software is
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+ furnished to do so, subject to the following conditions:
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+
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+ The above copyright notice and this permission notice shall be included in all
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+ copies or substantial portions of the Software.
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+
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+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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+ SOFTWARE.
README ADDED
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+ Information
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+ ===========
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+
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+ This is the YCB-Video dataset [1] for 6D object pose estimation.
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+ It provides accurate 6D poses of 21 objects from the YCB dataset [2]
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+ observed in 92 videos with 133,827 frames.
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+
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+ This project was funded in part by Siemens and by NSF
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+ STTR grant 63-5197 with Lula Robotics.
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+
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+
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+ How to Cite
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+ ===========
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+
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+ @article{xiang2017posecnn,
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+ author = {Xiang, Yu and Schmidt, Tanner and Narayanan, Venkatraman and Fox, Dieter},
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+ title = {PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes},
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+ journal = {arXiv preprint arXiv:1711.00199},
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+ year = {2017}
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+ }
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+
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+
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+ Basic Usage
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+ ===========
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+
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+ The description of the directories in this package:
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+
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+ 1. cameras. The camera parameters used to capture the videos. asus-uw.json for video 0000 ~ 0059, asus-cmu.json for video 0060 ~ 0091.
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+ 2. data. The 92 videos in the dataset.
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+ 3. data_syn. 80,000 synthetic images of the 21 YCB objects.
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+ 4. image_sets. Separation of the videos into training set (train.txt) and the testing set (val.txt, keyframe.txt).
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+ 5. keyframes. Keyframe indexes of the 12 testing videos.
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+ 6. models. 3D models of the 21 YCB objects.
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+ 7. pairs. Stereo pair indexes of the 12 testing videos.
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+ 8. poses. All the 6D poses of the 21 YCB objects in the dataset (quaternion + translation).
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+
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+ References
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+ ==========
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+
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+ [1] Y. Xiang, T. Schmidt, V. Narayanan and D. Fox.
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+ PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes. In arXiv:1711.00199, 2017.
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+
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+ [2] B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar. The YCB object and model set: Towards common benchmarks
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+ for manipulation research. In International Conference on Advanced Robotics (ICAR), pp. 510–517, 2015.
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