论文标题
带有支柱级亲和力的无提示龙位圆形分割
Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity
论文作者
论文摘要
我们为LIDAR PANOPTIC分割提出了一个简单而有效的无提案架构。我们使用基于支柱的鸟类视图表示，在单个网络中共同优化语义分割和类不足的实例分类。实例分类头学会了支柱之间的成对亲和力，以确定支柱是否属于同一实例。我们进一步提出了一种局部聚类算法来通过合并语义分割和亲和力预测来传播实例ID。我们在Nuscenes数据集上进行的实验表明，我们的方法的表现优于先前的无建议方法，并且与基于建议的方法相媲美，该方法需要从对象检测中进行额外的注释。
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.
