We present a photo-realistic training and evaluation simulator (Sim4CV) 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. The supplementary video can be viewed a this https URL

本研究介绍了一个基于虚幻引擎的逼真仿真训练和评估系统 ——Sim4CV，它可以应用于计算机视觉领域的多个方向，例如自主飞行器追踪移动物体和自动驾驶，该系统可以自动生成真实感强的合成数据集来拓展现有的真实世界数据集。