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 25% 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.

翻译：随着自动驾驶产业的兴起，视觉感知系统的域自适应因其成本节约潜力成为重要研究方向。此前大量工作聚焦于合成到真实场景下的域自适应语义分割。然而，尽管全景分割是感知系统的关键输出，却长期被域自适应领域所忽视。为此，我们重新审视了其他领域中性能优异的域自适应策略，将其适配至全景分割任务，并验证其能有效增强全景域自适应性能。进一步地，我们研究了全景分割网络的设计，提出了一种专为域自适应全景分割设计的新颖架构（EDAPS）。该架构采用共享且具有域鲁棒性的Transformer编码器，以促进语义特征与实例特征的联合域自适应，同时为域自适应语义分割和实例分割的特定需求定制了任务专属解码器。由此，在具有挑战性的全景分割基准测试中，性能差距被显著缩小。EDAPS大幅提升了全景分割无监督域适应的最先进性能：在SYNTHIA-to-Cityscapes上提升25%，在更具挑战性的SYNTHIA-to-Mapillary Vistas上甚至提升72%。实现代码已开源至https://github.com/susaha/edaps。