# All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes

Source: [All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes](<https://www.sota2.com/research/paper/68fa78331e209131cea7725d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:00:49.909Z

Authors: Jose L. G\\'omez, Manuel Silva, Antonio Seoane, Agn\\\`es Borr\\'as, Mario Noriega, Germ\\'an Ros, Jose A. Iglesias-Guitian, Antonio M. L\\'opez

Published: 2023-12-19T14:09:12.000Z

## 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 '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).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2312.12176>)

[PDF](<https://arxiv.org/pdf/2312.12176.pdf>)

[Implementation](<https://www.sota2.com/www.urbansyn.org>)