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.

中文翻译：

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Sim4CV：用于计算机视觉应用的逼真模拟器

我们提出了一个逼真的训练和评估模拟器 (Sim4CV) (http://www.sim4cv.org)，在计算机视觉的各个领域都有广泛的应用。该模拟器建立在虚幻引擎之上，在不同的城市和郊区 3D 环境中集成了全功能的基于物理的汽车、无人驾驶飞行器 (UAV) 和动画人物。我们通过两个案例研究展示了模拟器的多功能性：基于无人机的自主移动物体跟踪和使用监督学习的自主驾驶。该模拟器将几种最先进的跟踪算法与基准评估工具和用于训练车辆自动驾驶的深度神经网络架构完全集成。