Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity
This addresses efficient 3D scene understanding for autonomous driving, offering a simpler alternative to complex proposal-based approaches.
The paper tackles lidar panoptic segmentation by proposing a proposal-free architecture that jointly optimizes semantic segmentation and instance classification using pillar-level affinity, achieving performance comparable to proposal-based methods on the nuScenes dataset.
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.