| Information |
| =========== |
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| This is the YCB-Video dataset [1] for 6D object pose estimation. |
| It provides accurate 6D poses of 21 objects from the YCB dataset [2] |
| observed in 92 videos with 133,827 frames. |
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| This project was funded in part by Siemens and by NSF |
| STTR grant 63-5197 with Lula Robotics. |
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| How to Cite |
| =========== |
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| @article{xiang2017posecnn, |
| author = {Xiang, Yu and Schmidt, Tanner and Narayanan, Venkatraman and Fox, Dieter}, |
| title = {PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes}, |
| journal = {arXiv preprint arXiv:1711.00199}, |
| year = {2017} |
| } |
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| Basic Usage |
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| The description of the directories in this package: |
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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. |
| 2. data. The 92 videos in the dataset. |
| 3. data_syn. 80,000 synthetic images of the 21 YCB objects. |
| 4. image_sets. Separation of the videos into training set (train.txt) and the testing set (val.txt, keyframe.txt). |
| 5. keyframes. Keyframe indexes of the 12 testing videos. |
| 6. models. 3D models of the 21 YCB objects. |
| 7. pairs. Stereo pair indexes of the 12 testing videos. |
| 8. poses. All the 6D poses of the 21 YCB objects in the dataset (quaternion + translation). |
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| References |
| ========== |
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| [1] Y. Xiang, T. Schmidt, V. Narayanan and D. Fox. |
| PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes. In arXiv:1711.00199, 2017. |
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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 |
| for manipulation research. In International Conference on Advanced Robotics (ICAR), pp. 510–517, 2015. |
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