UE4Sim: A Photo-Realistic Simulator for Computer Vision Applications

Handle URI:
http://hdl.handle.net/10754/626562
Title:
UE4Sim: A Photo-Realistic Simulator for Computer Vision Applications
Authors:
Mueller, Matthias; Casser, Vincent; Lahoud, Jean; Smith, Neil; Ghanem, Bernard ( 0000-0002-5534-587X )
Abstract:
We present a photo-realistic training and evaluation simulator (UE4Sim) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator integrates full featured physics based cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. The simulator fully integrates both several state-of-the-art tracking algorithms with a benchmark evaluation tool and a deep neural network (DNN) architecture for training vehicles to drive autonomously. It generates synthetic photo-realistic datasets with automatic ground truth annotations to easily extend existing real-world datasets and provides extensive synthetic data variety through its ability to reconfigure synthetic worlds on the fly using an automatic world generation tool.
KAUST Department:
Visual Computing Center (VCC)
Publisher:
arXiv
Issue Date:
19-Aug-2017
ARXIV:
arXiv:1708.05869
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1708.05869v1; http://arxiv.org/pdf/1708.05869v1
Appears in Collections:
Other/General Submission; Visual Computing Center (VCC)

Full metadata record

DC FieldValue Language
dc.contributor.authorMueller, Matthiasen
dc.contributor.authorCasser, Vincenten
dc.contributor.authorLahoud, Jeanen
dc.contributor.authorSmith, Neilen
dc.contributor.authorGhanem, Bernarden
dc.date.accessioned2017-12-28T07:32:16Z-
dc.date.available2017-12-28T07:32:16Z-
dc.date.issued2017-08-19en
dc.identifier.urihttp://hdl.handle.net/10754/626562-
dc.description.abstractWe present a photo-realistic training and evaluation simulator (UE4Sim) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator integrates full featured physics based cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. The simulator fully integrates both several state-of-the-art tracking algorithms with a benchmark evaluation tool and a deep neural network (DNN) architecture for training vehicles to drive autonomously. It generates synthetic photo-realistic datasets with automatic ground truth annotations to easily extend existing real-world datasets and provides extensive synthetic data variety through its ability to reconfigure synthetic worlds on the fly using an automatic world generation tool.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1708.05869v1en
dc.relation.urlhttp://arxiv.org/pdf/1708.05869v1en
dc.rightsArchived with thanks to arXiven
dc.titleUE4Sim: A Photo-Realistic Simulator for Computer Vision Applicationsen
dc.typePreprinten
dc.contributor.departmentVisual Computing Center (VCC)en
dc.eprint.versionPre-printen
dc.identifier.arxividarXiv:1708.05869en
kaust.authorMueller, Matthiasen
kaust.authorCasser, Vincenten
kaust.authorLahoud, Jeanen
kaust.authorSmith, Neilen
kaust.authorGhanem, Bernarden
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