  In recent years, compact and efficient scene understanding representations
have gained popularity in increasing situational awareness and autonomy of
robotic systems. In this work, we illustrate the concept of a panoptic edge
segmentation and propose PENet, a novel detection network called that combines
semantic edge detection and instance-level perception into a compact panoptic
edge representation. This is obtained through a joint network by multi-task
learning that concurrently predicts semantic edges, instance centers and offset
flow map without bounding box predictions exploiting the cross-task
correlations among the tasks. The proposed approach allows extending semantic
edge detection to panoptic edge detection which encapsulates both
category-aware and instance-aware segmentation. We validate the proposed
panoptic edge segmentation method and demonstrate its effectiveness on the
real-world Cityscapes dataset.
