A fast and accurate panoptic segmentation system for LiDAR point clouds is crucial for autonomous driving vehicles to understand the surrounding objects and scenes. Existing approaches usually rely on proposals or clustering to segment foreground instances. As a result, they struggle to achieve real-time performance. In this paper, we propose a novel real-time end-to-end panoptic segmentation network for LiDAR point clouds, called CPSeg. In particular, CPSeg comprises a shared encoder, a dual-decoder, and a cluster-free instance segmentation head, which is able to dynamically pillarize foreground points according to the learned embedding. Then, it acquires instance labels by finding connected pillars with a pairwise embedding comparison. Thus, the conventional proposal-based or clustering-based instance segmentation is transformed into a binary segmentation problem on the pairwise embedding comparison matrix. To help the network regress instance embedding, a fast and deterministic depth completion algorithm is proposed to calculate the surface normal of each point cloud in real-time. The proposed method is benchmarked on two large-scale autonomous driving datasets: SemanticKITTI and nuScenes. Notably, extensive experimental results show that CPSeg achieves state-of-the-art results among real-time approaches on both datasets.

翻译：摘要：针对LiDAR点云的快速精确全景分割系统对于自动驾驶车辆理解周围物体与场景至关重要。现有方法通常依赖提议或聚类来分割前景实例，因此难以实现实时性能。本文提出一种名为CPSeg的新型实时端到端全景分割网络，专用于LiDAR点云。具体而言，CPSeg由共享编码器、双解码器及无聚类实例分割头组成，能够根据学习到的嵌入向量对前景点进行动态柱化处理。随后，通过两两嵌入比较寻找连通柱体以获取实例标签。由此，传统的基于提议或聚类的实例分割被转化为在成对嵌入比较矩阵上的二值分割问题。为辅助网络回归实例嵌入，本文提出一种快速且确定性的深度补全算法，可实时计算每个点云的表面法向量。所提方法在两个大规模自动驾驶数据集SemanticKITTI和nuScenes上进行了基准测试。值得注意的是，大量实验结果表明，CPSeg在两类数据集的实时方法中均达到了最先进的性能。