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UnrealGT: Using Unreal Engine to Generate Ground Truth Datasets

Pollok, Thomas; Junglas, Lorenz; Ruf, Boitumelo; Schumann, Arne

Abstract:
Large amounts of data have become an essential requirement in the development of modern computer vision algorithms, e.g. the training of neural networks. Due to data protection laws, overflight permissions for UAVs or expensive equipment, data collection is often a costly and time-consuming task. Especially, if the ground truth is generated by manually annotating the collected data. By means of synthetic data generation, large amounts of image- and metadata can be extracted directly from a virtual scene, which in turn can be customized to meet the specific needs of the algorithm or the use-case. Furthermore, the use of virtual objects avoids problems that might arise due to data protection issues and does not require the use of expensive sensors. In this work we propose a framework for synthetic test data generation utilizing the Unreal Engine. The Unreal Engine provides a simulation environment that allows one to simulate complex situations in a virtual world, such as data acquisition with UAVs or autonomous diving. However, our process is agnostic to the computer vision task for which the data is generated and, thus, can be used to create generic datasets. ... mehr



Originalveröffentlichung
DOI: 10.1007/978-3-030-33720-9_52
Dimensions
Zitationen: 1
Zugehörige Institution(en) am KIT Fakultät für Bauingenieur-, Geo- und Umweltwissenschaften (BGU)
Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2019
Sprache Englisch
Identifikator ISBN: 978-3-030-33719-3
KITopen-ID: 1000104541
Erschienen in Advances in Visual Computing : 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I. Ed.: George Bebis
Verlag Springer
Seiten 670–682
Nachgewiesen in Dimensions
Scopus
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