  Panoptic Segmentation aims to provide an understanding of background (stuff)
and instances of objects (things) at a pixel level. It combines the separate
tasks of semantic segmentation (pixel level classification) and instance
segmentation to build a single unified scene understanding task. Typically,
panoptic segmentation is derived by combining semantic and instance
segmentation tasks that are learned separately or jointly (multi-task
networks). In general, instance segmentation networks are built by adding a
foreground mask estimation layer on top of object detectors or using instance
clustering methods that assign a pixel to an instance center. In this work, we
present a fully convolution neural network that learns instance segmentation
from semantic segmentation and instance contours (boundaries of things).
Instance contours along with semantic segmentation yield a boundary aware
semantic segmentation of things. Connected component labeling on these results
produces instance segmentation. We merge semantic and instance segmentation
results to output panoptic segmentation. We evaluate our proposed method on the
CityScapes dataset to demonstrate qualitative and quantitative performances
along with several ablation studies. Our overview video can be accessed from
url:https://youtu.be/wBtcxRhG3e0.
