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We introduce Synscapes -- a synthetic dataset for street scene parsingcreated using photorealistic rendering techniques, and show state-of-the-artresults for training and validation as well as new types of analysis. We studythe behavior of networks trained on real data when performing inference onsynthetic data: a key factor in determining the equivalence of simulationenvironments. We also compare the behavior of networks trained on syntheticdata and evaluated on real-world data. Additionally, by analyzing pre-trained,existing segmentation and detection models, we illustrate how uncorrelatedimages along with a detailed set of annotations open up new avenues foranalysis of computer vision systems, providing fine-grain information about howa model's performance changes according to factors such as distance, occlusionand relative object orientation.

Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing