Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications

• Published in: International journal of computer vision Vol. 126; no. 9; pp. 902 - 919
• Main Authors: Müller, Matthias, Casser, Vincent, Lahoud, Jean, Smith, Neil, Ghanem, Bernard
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2018; Springer; Springer Nature B.V
• Subjects: Annotations; Artificial Intelligence; Artificial neural networks; Automobiles; Computer Imaging; Computer Science; Computer simulation; Computer vision; Datasets; Ground truth; Image Processing and Computer Vision; Machine learning; Neural networks; Pattern Recognition; Pattern Recognition and Graphics; Tracking; Training; Unmanned aerial vehicles; Vision; Deep learning; Unreal Engine 4; Autonomous driving; Imitation learning; Object tracking; Simulator
• ISSN: 0920-5691; 1573-1405
• DOI: 10.1007/s11263-018-1073-7

We present a photo-realistic training and evaluation simulator (Sim4CV) ( http://www.sim4cv.org ) 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 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.