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).

翻译：我们提出UrbanSyn，一个通过半程序化合成城市驾驶场景生成的照片级真实感数据集。该数据集基于高质量几何体与材质构建，提供像素级真值标注，包括深度、语义分割、实例分割，以及带有目标边界框和遮挡程度的实例级标注。UrbanSyn与GTAV和Synscapes数据集互补，共同构成我们所谓的“三剑客”。我们展示了三剑客在无监督域自适应图像语义分割中的价值。在真实世界数据集Cityscapes、Mapillary Vistas和BDD100K上的实验结果刷新了基准，这主要归功于UrbanSyn。我们公开并免费提供UrbanSyn数据集（www.urbansyn.org）。