Single-Shot Panoptic Segmentation

Mark Weber, Jonathon Luiten, Bastian Leibe
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020

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. 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 21.8 FPS, opening up panoptic segmentation to a broader field of applications.

» Show BibTeX

title={Single-Shot Panoptic Segmentation},
author={Weber, Mark and Luiten, Jonathon and Leibe, Bastian},
journal={arXiv preprint arXiv:1911.00764},

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