  Panoptic segmentation is one of the most challenging scene parsing tasks,
combining the tasks of semantic segmentation and instance segmentation. While
much progress has been made, few works focus on the real-time application of
panoptic segmentation methods. In this paper, we revisit the recently
introduced K-Net architecture. We propose vital changes to the architecture,
training, and inference procedure, which massively decrease latency and improve
performance. Our resulting RT-K-Net sets a new state-of-the-art performance for
real-time panoptic segmentation methods on the Cityscapes dataset and shows
promising results on the challenging Mapillary Vistas dataset. On Cityscapes,
RT-K-Net reaches 60.2 % PQ with an average inference time of 32 ms for full
resolution 1024x2048 pixel images on a single Titan RTX GPU. On Mapillary
Vistas, RT-K-Net reaches 33.2 % PQ with an average inference time of 69 ms.
Source code is available at https://github.com/markusschoen/RT-K-Net.
