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

DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange