  Autonomous vehicles clearly benefit from the expanded Field of View (FoV) of
360-degree sensors, but modern semantic segmentation approaches rely heavily on
annotated training data which is rarely available for panoramic images. We look
at this problem from the perspective 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. To achieve
this, we formalize the task of unsupervised domain adaptation for panoramic
semantic segmentation and collect DensePASS - a novel densely annotated dataset
for panoramic segmentation under cross-domain conditions, specifically built to
study the Pinhole-to-Panoramic domain shift 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 (i.e. pinhole)
domain. Since data-driven models are especially susceptible to changes in data
distribution, we introduce P2PDA - a generic framework for Pinhole-to-Panoramic
semantic segmentation which addresses the challenge of domain divergence with
different variants of attention-augmented domain adaptation modules, enabling
the transfer in output-, feature-, and feature confidence spaces. P2PDA
intertwines uncertainty-aware adaptation using confidence values regulated
on-the-fly through attention heads with discrepant predictions. Our framework
facilitates context exchange when learning domain correspondences and
dramatically improves the adaptation performance of accuracy- and
efficiency-focused models. Comprehensive experiments verify that our framework
clearly surpasses unsupervised domain adaptation- and specialized panoramic
segmentation approaches.
