Dataset Open Access

UnLoc dataset (Synthetic + Real)

Loing, Vianney


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    "description": "<p>This dataset contains the synthetic and real data used in the article &quot; Virtual Training for a Real Application: Accurate Object-Robot Relative Localization Without Calibration &quot; 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.&nbsp;</p>\n\n<p>It consists of a dataset composed of synthetic pictures for training the CNNs and a dataset of real pictures for evaluation.&nbsp;</p>\n\n<p>The &quot;synthetic&quot; dataset is composed 3 sub-datasets (each of them composed of thousands of synthetic pictures and corresponding groundtruth) for training :&nbsp;</p>\n\n<ol>\n\t<li>a dataset for coarse relative localization subtask : coarse_estimation_data (~14.6 GB when extracted)</li>\n\t<li>a dataset for the tool localization subtask :&nbsp;tool_detection_data&nbsp;(~4.3&nbsp;GB when extracted)</li>\n\t<li>a dataset for the fine relative localization subtask :&nbsp;fine_estimation_data&nbsp;(~13.3 GB when extracted)</li>\n</ol>\n\n<p>These sub-datasets are composed of raw data as well as post-treated data ready to be used for training CNNs,&nbsp;in CSV format and in Torch format (.t7).&nbsp;</p>\n\n<p>The &quot;real&quot; dataset is&nbsp;composed of real pictures&nbsp;with a precisely localized cuboid block for evaluation only : UnLoc_real (~2.5&nbsp;GB when extracted).&nbsp;</p>\n\n<p>More information available on the project page :&nbsp;<a href=\"http://imagine.enpc.fr/~loingvi/unloc/\">http://imagine.enpc.fr/~loingvi/unloc/</a></p>", 
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    "title": "UnLoc dataset (Synthetic + Real)", 
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    "references": [
      "Loing et al. (2018). Dataset associated to arXiv:1902.02711"
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    "keywords": [
      "uncalibrated relative localization", 
      "pose estimation", 
      "synthetic data", 
      "virtual training", 
      "robotics"
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    "publication_date": "2019-02-12", 
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