  In this work we introduce a novel, CNN-based architecture that can be trained
end-to-end to deliver seamless scene segmentation results. Our goal is to
predict consistent semantic segmentation and detection results by means of a
panoptic output format, going beyond the simple combination of independently
trained segmentation and detection models. The proposed architecture takes
advantage of a novel segmentation head that seamlessly integrates multi-scale
features generated by a Feature Pyramid Network with contextual information
conveyed by a light-weight DeepLab-like module. As additional contribution we
review the panoptic metric and propose an alternative that overcomes its
limitations when evaluating non-instance categories. Our proposed network
architecture yields state-of-the-art results on three challenging street-level
datasets, i.e. Cityscapes, Indian Driving Dataset and Mapillary Vistas.
