{"id":2418,"date":"2015-12-20T09:14:00","date_gmt":"2015-12-20T09:14:00","guid":{"rendered":"https:\/\/mapir.isa.uma.es\/mapirwebsite_wordpress\/?p=2418"},"modified":"2021-12-20T09:19:41","modified_gmt":"2021-12-20T09:19:41","slug":"labeling-datasets-with-olt","status":"publish","type":"post","link":"https:\/\/mapir.isa.uma.es\/mapirwebsite\/?p=2418","title":{"rendered":"Labeling datasets with OLT"},"content":{"rendered":"\n

The\u00a0Object Labeling Toolkit (OLT)<\/strong><\/a>\u00a0consists of a number of software applications for performing an effortless labeling of datasets, concretely those containing sequences of RGB-D observations.<\/p>\n\n\n\n\n\n\n\n

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If you use this software, please cite it through:<\/p>\n\n\n\n

      @INPROCEEDINGS{Ruiz-Sarmiento-ECMR-2015,\n           author = {Ruiz-Sarmiento, J. R. and Galindo, Cipriano and Gonz{\\'{a}}lez-Jim{\\'{e}}nez, Javier},\n            title = {OLT: A Toolkit for Object Labeling Applied to Robotic RGB-D Datasets},\n        booktitle = {European Conference on Mobile Robots},\n             year = {2015},\n         location = {Lincoln, UK}\n      }<\/pre>\n\n\n\n
For getting the toolkit and further information please check\u00a0the project webpage at GitHub<\/a>. OLT aims at facilitate the labeling of arbitrarily large sequences of RGB-D observations. For that, the point clouds from the RGB-D observations are registered in order to reconstruct the captured scene. Then, the toolkit provides an application to easily annotate objects within that reconstruction by fitting boxes to them:<\/p>\n\n\n\n
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Example of a kitchen scene being annotated.<\/em><\/figcaption><\/figure><\/div>\n\n\n\n
These annotations are finally propagated to each individual RGB-D observation in the sequence, resulting in a dense labeling of their RGB and depth information.<\/p>\n\n\n\n
The main software components (appliations) of the toolkit are:<\/p>\n\n\n\n