We propose to harness the potential of simulation for the semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is trained without any data of target domains and tested on the unseen target domains. To this end, we propose a new approach of domain randomization and pyramid consistency to learn a model with high generalizability. First, we propose to randomize the synthetic images with the styles of real images in terms of visual appearances using auxiliary datasets, in order to effectively learn domain-invariant representations. Second, we further enforce pyramid consistency across different "stylized" images and within an image, in order to learn domain-invariant and scale-invariant features, respectively. Extensive experiments are conducted on the generalization from GTA and SYNTHIA to Cityscapes, BDDS and Mapillary; and our method achieves superior results over the state-of-the-art techniques. Remarkably, our generalization results are on par with or even better than those obtained by state-of-the-art simulation-to-real domain adaptation methods, which access the target domain data at training time.

本文提出一种使用模拟技术进行自动驾驶场景语义分割的方法，通过域泛化的方式进行测试并实现了高度通用性，其中使用了域随机化和金字塔一致性两种技术，实现了域不变的特征表示。经过在GTA，SYNTHIA到Cityscapes，BDDS和Mapillary的广泛实验，本方法在一些状态和最先进的仿真转现领域自适应方法的测试结果表明，本方法实现了有竞争力、甚至优于其他方法的结果。

领域随机化和金字塔一致性: 无需访问目标域数据的模拟到实际泛化