  We present a novel end-to-end single-shot method that segments countable
object instances (things) as well as background regions (stuff) into a
non-overlapping panoptic segmentation at almost video frame rate. Current
state-of-the-art methods are far from reaching video frame rate and mostly rely
on merging instance segmentation with semantic background segmentation, making
them impractical to use in many applications such as robotics. Our approach
relaxes this requirement by using an object detector but is still able to
resolve inter- and intra-class overlaps to achieve a non-overlapping
segmentation. On top of a shared encoder-decoder backbone, we utilize multiple
branches for semantic segmentation, object detection, and instance center
prediction. Finally, our panoptic head combines all outputs into a panoptic
segmentation and can even handle conflicting predictions between branches as
well as certain false predictions. Our network achieves 32.6% PQ on MS-COCO at
23.5 FPS, opening up panoptic segmentation to a broader field of applications.
