  In this work, we present an end-to-end network for fast panoptic
segmentation. This network, called Fast Panoptic Segmentation Network (FPSNet),
does not require computationally costly instance mask predictions or merging
heuristics. This is achieved by casting the panoptic task into a custom dense
pixel-wise classification task, which assigns a class label or an instance id
to each pixel. We evaluate FPSNet on the Cityscapes and Pascal VOC datasets,
and find that FPSNet is faster than existing panoptic segmentation methods,
while achieving better or similar panoptic segmentation performance. On the
Cityscapes validation set, we achieve a Panoptic Quality score of 55.1%, at
prediction times of 114 milliseconds for images with a resolution of 1024x2048
pixels. For lower resolutions of the Cityscapes dataset and for the Pascal VOC
dataset, FPSNet runs at 22 and 35 frames per second, respectively.
