UE4Sim: A Photo-Realistic Simulator for Computer Vision Applications
Type
PreprintKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Visual Computing Center (VCC)
Date
2017-08-19Permanent link to this record
http://hdl.handle.net/10754/626562
Metadata
Show full item recordAbstract
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.Publisher
arXivarXiv
1708.05869Additional Links
http://arxiv.org/abs/1708.05869v1http://arxiv.org/pdf/1708.05869v1