  Panoptic segmentation of point clouds is a crucial task that enables
autonomous vehicles to comprehend their vicinity using their highly accurate
and reliable LiDAR sensors. Existing top-down approaches tackle this problem by
either combining independent task-specific networks or translating methods from
the image domain ignoring the intricacies of LiDAR data and thus often
resulting in sub-optimal performance. In this paper, we present the novel
top-down Efficient LiDAR Panoptic Segmentation (EfficientLPS) architecture that
addresses multiple challenges in segmenting LiDAR point clouds including
distance-dependent sparsity, severe occlusions, large scale-variations, and
re-projection errors. EfficientLPS comprises of a novel shared backbone that
encodes with strengthened geometric transformation modeling capacity and
aggregates semantically rich range-aware multi-scale features. It incorporates
new scale-invariant semantic and instance segmentation heads along with the
panoptic fusion module which is supervised by our proposed panoptic periphery
loss function. Additionally, we formulate a regularized pseudo labeling
framework to further improve the performance of EfficientLPS by training on
unlabelled data. We benchmark our proposed model on two large-scale LiDAR
datasets: nuScenes, for which we also provide ground truth annotations, and
SemanticKITTI. Notably, EfficientLPS sets the new state-of-the-art on both
these datasets.
