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).

通过半程序生成的合成城市驾驶场景，我们介绍了UrbanSyn数据集，该数据集具有逼真的光线效果。UrbanSyn使用高质量的几何和材料开发，提供了像素级别的地面真实性，包括深度、语义分割和实例分割，并带有物体边界框和遮挡程度。它与GTAV和Synscapes数据集相互补充，形成了我们所定义的“三剑客”。我们展示了“三剑客”在图像语义分割的无监督域适应中的价值。在真实世界的数据集Cityscapes、Mapillary Vistas和BDD100K上的结果建立了新的基准，这主要归功于UrbanSyn。我们将UrbanSyn开放并免费提供（www.urbansyn.org)。

同心并进：UrbanSyn数据集，合成行车场景的第三位三剑客