  Deep learning has rapidly transformed the state of the art algorithms used to
address a variety of problems in computer vision and robotics. These
breakthroughs have relied upon massive amounts of human annotated training
data. This time consuming process has begun impeding the progress of these deep
learning efforts. This paper describes a method to incorporate photo-realistic
computer images from a simulation engine to rapidly generate annotated data
that can be used for the training of machine learning algorithms. We
demonstrate that a state of the art architecture, which is trained only using
these synthetic annotations, performs better than the identical architecture
trained on human annotated real-world data, when tested on the KITTI data set
for vehicle detection. By training machine learning algorithms on a rich
virtual world, real objects in real scenes can be learned and classified using
synthetic data. This approach offers the possibility of accelerating deep
learning's application to sensor-based classification problems like those that
appear in self-driving cars. The source code and data to train and validate the
networks described in this paper are made available for researchers.
