  In this work, we introduce panoramic panoptic segmentation as the most
holistic scene understanding both in terms of field of view and image level
understanding for standard camera based input. A complete surrounding
understanding provides a maximum of information to the agent, which is
essential for any intelligent vehicle in order to make informed decisions in a
safety-critical dynamic environment such as real-world traffic. In order to
overcome the lack of annotated panoramic images, we propose a framework which
allows model training on standard pinhole images and transfers the learned
features to a different domain. Using our proposed method, we manage to achieve
significant improvements of over 5% measured in PQ over non-adapted models on
our Wild Panoramic Panoptic Segmentation (WildPPS) dataset. We show that our
proposed Panoramic Robust Feature (PRF) framework is not only suitable to
improve performance on panoramic images but can be beneficial whenever model
training and deployment are executed on data taken from different
distributions. As an additional contribution, we publish WildPPS: The first
panoramic panoptic image dataset to foster progress in surrounding perception.
