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In training deep neural networks for semantic segmentation, the main limitingfactor is the low amount of ground truth annotation data that is available incurrently existing datasets. The limited availability of such data is due tothe time cost and human effort required to accurately and consistently labelreal images on a pixel level. Modern sandbox video game engines provide openworld environments where traffic and pedestrians behave in a pseudo-realisticmanner. This caters well to the collection of a believable road-scene dataset.Utilizing open-source tools and resources found in single-player moddingcommunities, we provide a method for persistent, ground truth, asset annotationof a game world. By collecting a synthetic dataset containing upwards of$1,000,000$ images, we demonstrate real-time, on-demand, ground truth dataannotation capability of our method. Supplementing this synthetic data toCityscapes dataset, we show that our data generation method providesqualitative as well as quantitative improvements---for training networks---overprevious methods that use video games as surrogate.

Unlimited Road-scene Synthetic Annotation (URSA) Dataset