  Panoptic segmentation presents a new challenge in exploiting the merits of
both detection and segmentation, with the aim of unifying instance segmentation
and semantic segmentation in a single framework. However, an efficient solution
for panoptic segmentation in the emerging domain of LiDAR point cloud is still
an open research problem and is very much under-explored. In this paper, we
present a fast and robust LiDAR point cloud panoptic segmentation framework,
referred to as Panoptic-PolarNet. We learn both semantic segmentation and
class-agnostic instance clustering in a single inference network using a polar
Bird's Eye View (BEV) representation, enabling us to circumvent the issue of
occlusion among instances in urban street scenes. To improve our network's
learnability, we also propose an adapted instance augmentation technique and a
novel adversarial point cloud pruning method. Our experiments show that
Panoptic-PolarNet outperforms the baseline methods on SemanticKITTI and
nuScenes datasets with an almost real-time inference speed. Panoptic-PolarNet
achieved 54.1% PQ in the public SemanticKITTI panoptic segmentation leaderboard
and leading performance for the validation set of nuScenes.
