@Misc{JoseLuisGomez2023,
author="Jose Luis Gomez
and Manuel Silva
and Antonio Seoane
and Agnes Borras
and Mario Noriega
and German Ros
and Jose Antonio Iglesias
and Antonio Lopez",
title="All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes",
year="2023",
abstract="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 {\textquoteright}Three Musketeers{\textquoteright}. 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 (this http URL).",
optnote="ADAS",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=4015), last updated on Fri, 14 Jun 2024 10:05:18 +0200",
opturl="https://arxiv.org/abs/2312.12176",
file=":http://refbase.cvc.uab.es/files/GSS2023.pdf:PDF"
}