  With autonomous industries on the rise, domain adaptation of the visual
perception stack is an important research direction due to the cost savings
promise. Much prior art was dedicated to domain-adaptive semantic segmentation
in the synthetic-to-real context. Despite being a crucial output of the
perception stack, panoptic segmentation has been largely overlooked by the
domain adaptation community. Therefore, we revisit well-performing domain
adaptation strategies from other fields, adapt them to panoptic segmentation,
and show that they can effectively enhance panoptic domain adaptation. Further,
we study the panoptic network design and propose a novel architecture (EDAPS)
designed explicitly for domain-adaptive panoptic segmentation. It uses a
shared, domain-robust transformer encoder to facilitate the joint adaptation of
semantic and instance features, but task-specific decoders tailored for the
specific requirements of both domain-adaptive semantic and instance
segmentation. As a result, the performance gap seen in challenging panoptic
benchmarks is substantially narrowed. EDAPS significantly improves the
state-of-the-art performance for panoptic segmentation UDA by a large margin of
20% on SYNTHIA-to-Cityscapes and even 72% on the more challenging
SYNTHIA-to-Mapillary Vistas. The implementation is available at
https://github.com/susaha/edaps.
