  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.
