  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. 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.
