  We propose a simple yet effective proposal-free architecture for lidar
panoptic segmentation. We jointly optimize both semantic segmentation and
class-agnostic instance classification in a single network using a pillar-based
bird's-eye view representation. The instance classification head learns
pairwise affinity between pillars to determine whether the pillars belong to
the same instance or not. We further propose a local clustering algorithm to
propagate instance ids by merging semantic segmentation and affinity
predictions. Our experiments on nuScenes dataset show that our approach
outperforms previous proposal-free methods and is comparable to proposal-based
methods which requires extra annotation from object detection.
