UnLoc dataset (Synthetic + Real)
Description
This dataset contains the synthetic and real data used in the article " Virtual Training for a Real Application: Accurate Object-Robot Relative Localization Without Calibration " to train 3 convolutional neural networks (CNNs) in order to perform uncalibrated relative localization of a cuboid block with respect to a robot, and to evaluate them.
It consists of a dataset composed of synthetic pictures for training the CNNs and a dataset of real pictures for evaluation.
The "synthetic" dataset is composed 3 sub-datasets (each of them composed of thousands of synthetic pictures and corresponding groundtruth) for training :
- a dataset for coarse relative localization subtask : coarse_estimation_data (~14.6 GB when extracted)
- a dataset for the tool localization subtask : tool_detection_data (~4.3 GB when extracted)
- a dataset for the fine relative localization subtask : fine_estimation_data (~13.3 GB when extracted)
These sub-datasets are composed of raw data as well as post-treated data ready to be used for training CNNs, in CSV format and in Torch format (.t7).
The "real" dataset is composed of real pictures with a precisely localized cuboid block for evaluation only : UnLoc_real (~2.5 GB when extracted).
More information available on the project page : http://imagine.enpc.fr/~loingvi/unloc/
Files
Files
(32.5 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:8e890c2a90cf0bbf1e381aa922fe9b8d
|
13.7 GB | Download |
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md5:e398604ce4295665498be4f55b9df092
|
12.4 GB | Download |
|
md5:26ab841be62963ba2037889b10bd9549
|
4.0 GB | Download |
|
md5:d19f95e7485b9c702beac02dc226aa07
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2.5 GB | Download |
Additional details
Related works
- Is supplemented by
- 10.1007/s11263-018-1102-6 (DOI)
References
- Loing et al. (2018). Dataset associated to arXiv:1902.02711