All for One, and One for All: UrbanSyn Dataset, the third Musketeer of
  Synthetic Driving Scenes
Jose L. Gómez Manuel Silva Antonio Seoane Agnès Borrás Mario Noriega Germán Ros Jose A. Iglesias-Guitian Antonio M. López
We introduce UrbanSyn, a photorealistic dataset acquired through
semi-procedurally generated synthetic urban driving scenarios. Developed using
high-quality geometry and materials, UrbanSyn provides pixel-level ground
truth, including depth, semantic segmentation, and instance segmentation with
object bounding boxes and occlusion degree. It complements GTAV and Synscapes
datasets to form what we coin as the 'Three Musketeers'. We demonstrate the
value of the Three Musketeers in unsupervised domain adaptation for image
semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary
Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn.
We make UrbanSyn openly and freely accessible (www.urbansyn.org).