  Convolutional Networks (ConvNets) excel at semantic segmentation and have
become a vital component for perception in autonomous driving. Enabling an
all-encompassing view of street-scenes, omnidirectional cameras present
themselves as a perfect fit in such systems. Most segmentation models for
parsing urban environments operate on common, narrow Field of View (FoV)
images. Transferring these models from the domain they were designed for to
360-degree perception, their performance drops dramatically, e.g., by an
absolute 30.0% (mIoU) on established test-beds. To bridge the gap in terms of
FoV and structural distribution between the imaging domains, we introduce
Efficient Concurrent Attention Networks (ECANets), directly capturing the
inherent long-range dependencies in omnidirectional imagery. In addition to the
learned attention-based contextual priors that can stretch across 360-degree
images, we upgrade model training by leveraging multi-source and
omni-supervised learning, taking advantage of both: Densely labeled and
unlabeled data originating from multiple datasets. To foster progress in
panoramic image segmentation, we put forward and extensively evaluate models on
Wild PAnoramic Semantic Segmentation (WildPASS), a dataset designed to capture
diverse scenes from all around the globe. Our novel model, training regimen and
multi-source prediction fusion elevate the performance (mIoU) to new
state-of-the-art results on the public PASS (60.2%) and the fresh WildPASS
(69.0%) benchmarks.
