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In this work, we introduce panoramic panoptic segmentation, as the mostholistic scene understanding, both in terms of Field of View (FoV) andimage-level understanding for standard camera-based input. A completesurrounding understanding provides a maximum of information to a mobile agent.This is essential information for any intelligent vehicle to make informeddecisions in a safety-critical dynamic environment such as real-world traffic.In order to overcome the lack of annotated panoramic images, we propose aframework which allows model training on standard pinhole images and transfersthe learned features to the panoramic domain in a cost-minimizing way. Thedomain shift from pinhole to panoramic images is non-trivial as large objectsand surfaces are heavily distorted close to the image border regions and lookdifferent across the two domains. Using our proposed method with densecontrastive learning, we manage to achieve significant improvements over anon-adapted approach. Depending on the efficient panoptic segmentationarchitecture, we can improve 3.5-6.5% measured in Panoptic Quality (PQ) overnon-adapted models on our established Wild Panoramic Panoptic Segmentation(WildPPS) dataset. Furthermore, our efficient framework does not need access tothe images of the target domain, making it a feasible domain generalizationapproach suitable for a limited hardware setting. As additional contributions,we publish WildPPS: The first panoramic panoptic image dataset to fosterprogress in surrounding perception and explore a novel training procedurecombining supervised and contrastive training.

Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning