  Intelligent vehicles clearly benefit from the expanded Field of View (FoV) of
the 360-degree sensors, but the vast majority of available semantic
segmentation training images are captured with pinhole cameras. In this work,
we look at this problem through the lens of domain adaptation and bring
panoramic semantic segmentation to a setting, where labelled training data
originates from a different distribution of conventional pinhole camera images.
First, we formalize the task of unsupervised domain adaptation for panoramic
semantic segmentation, where a network trained on labelled examples from the
source domain of pinhole camera data is deployed in a different target domain
of panoramic images, for which no labels are available. To validate this idea,
we collect and publicly release DensePASS - a novel densely annotated dataset
for panoramic segmentation under cross-domain conditions, specifically built to
study the Pinhole-to-Panoramic transfer and accompanied with pinhole camera
training examples obtained from Cityscapes. DensePASS covers both, labelled-
and unlabelled 360-degree images, with the labelled data comprising 19 classes
which explicitly fit the categories available in the source domain (i.e.
pinhole) data. To meet the challenge of domain shift, we leverage the current
progress of attention-based mechanisms and build a generic framework for
cross-domain panoramic semantic segmentation based on different variants of
attention-augmented domain adaptation modules. Our framework facilitates
information exchange at local- and global levels when learning the domain
correspondences and improves the domain adaptation performance of two standard
segmentation networks by 6.05% and 11.26% in Mean IoU.
