Dataset Open Access

UnLoc dataset (Synthetic + Real)

Loing, Vianney


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "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>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Ecole des Ponts ParisTech", 
      "@id": "https://orcid.org/0000-0003-4802-8208", 
      "@type": "Person", 
      "name": "Loing, Vianney"
    }
  ], 
  "url": "https://zenodo.org/record/2563622", 
  "datePublished": "2019-02-12", 
  "version": "1.0.0", 
  "keywords": [
    "uncalibrated relative localization", 
    "pose estimation", 
    "synthetic data", 
    "virtual training", 
    "robotics"
  ], 
  "@context": "https://schema.org/", 
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  "identifier": "https://doi.org/10.5281/zenodo.2563622", 
  "@id": "https://doi.org/10.5281/zenodo.2563622", 
  "@type": "Dataset", 
  "name": "UnLoc dataset (Synthetic + Real)"
}
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