Dataset Open Access

UnLoc dataset (Synthetic + Real)

Loing, Vianney


MARC21 XML Export

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    <subfield code="a">&lt;p&gt;This dataset contains the synthetic and real data used in the article &amp;quot; Virtual Training for a Real Application: Accurate Object-Robot Relative Localization Without Calibration &amp;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.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;It consists of a dataset composed of synthetic pictures for training the CNNs and a dataset of real pictures for evaluation.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The &amp;quot;synthetic&amp;quot; dataset is composed 3 sub-datasets (each of them composed of thousands of synthetic pictures and corresponding groundtruth) for training :&amp;nbsp;&lt;/p&gt;

&lt;ol&gt;
	&lt;li&gt;a dataset for coarse relative localization subtask : coarse_estimation_data (~14.6 GB when extracted)&lt;/li&gt;
	&lt;li&gt;a dataset for the tool localization subtask :&amp;nbsp;tool_detection_data&amp;nbsp;(~4.3&amp;nbsp;GB when extracted)&lt;/li&gt;
	&lt;li&gt;a dataset for the fine relative localization subtask :&amp;nbsp;fine_estimation_data&amp;nbsp;(~13.3 GB when extracted)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;These sub-datasets are composed of raw data as well as post-treated data ready to be used for training CNNs,&amp;nbsp;in CSV format and in Torch format (.t7).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The &amp;quot;real&amp;quot; dataset is&amp;nbsp;composed of real pictures&amp;nbsp;with a precisely localized cuboid block for evaluation only : UnLoc_real (~2.5&amp;nbsp;GB when extracted).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;More information available on the project page :&amp;nbsp;&lt;a href="http://imagine.enpc.fr/~loingvi/unloc/"&gt;http://imagine.enpc.fr/~loingvi/unloc/&lt;/a&gt;&lt;/p&gt;</subfield>
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