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.

我们提出了一种用于lidar全景分割的无需提案的简单而有效的架构。我们使用基于pillar的俯视图表示，联合优化语义分割和非类别实例分类。实例分类头部学习pillar之间的成对亲和力，以确定pillar是否属于同一实例。我们进一步提出了一种局部聚类算法，通过合并语义分割和亲和力预测来传播实例id。我们对nuScenes数据集进行的实验表明，我们的方法优于以前的无需提案的方法，并且与需要来自目标检测的额外注释的提案方法相当。

基于柱状体级别相似度的免提提议激光雷达全景分割