  We propose a simple, fast, and flexible framework to generate simultaneously
semantic and instance masks for panoptic segmentation. Our method, called
PanoNet, incorporates a clean and natural structure design that tackles the
problem purely as a segmentation task without the time-consuming detection
process. We also introduce position-sensitive embedding for instance grouping
by accounting for both object's appearance and its spatial location. Overall,
PanoNet yields high panoptic quality results of high-resolution Cityscapes
images in real-time, significantly faster than all other methods with
comparable performance. Our approach well satisfies the practical speed and
memory requirement for many applications like autonomous driving and augmented
reality.
