  Understanding the scene in which an autonomous robot operates is critical for
its competent functioning. Such scene comprehension necessitates recognizing
instances of traffic participants along with general scene semantics which can
be effectively addressed by the panoptic segmentation task. In this paper, we
introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that
consists of a shared backbone which efficiently encodes and fuses semantically
rich multi-scale features. We incorporate a new semantic head that aggregates
fine and contextual features coherently and a new variant of Mask R-CNN as the
instance head. We also propose a novel panoptic fusion module that congruously
integrates the output logits from both the heads of our EfficientPS
architecture to yield the final panoptic segmentation output. Additionally, we
introduce the KITTI panoptic segmentation dataset that contains panoptic
annotations for the popularly challenging KITTI benchmark. Extensive
evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset
demonstrate that our proposed architecture consistently sets the new
state-of-the-art on all these four benchmarks while being the most efficient
and fast panoptic segmentation architecture to date.
