3D point cloud panoptic segmentation is the combined task to (i) assign each point to a semantic class and (ii) separate the points in each class into object instances. Recently there has been an increased interest in such comprehensive 3D scene understanding, building on the rapid advances of semantic segmentation due to the advent of deep 3D neural networks. Yet, to date there is very little work about panoptic segmentation of outdoor mobile-mapping data, and no systematic comparisons. The present paper tries to close that gap. It reviews the building blocks needed to assemble a panoptic segmentation pipeline and the related literature. Moreover, a modular pipeline is set up to perform comprehensive, systematic experiments to assess the state of panoptic segmentation in the context of street mapping. As a byproduct, we also provide the first public dataset for that task, by extending the NPM3D dataset to include instance labels.

翻译：三维点云全景分割是一项综合任务，旨在（i）为每个点分配语义类别，以及（ii）将每个类别中的点分离成对象实例。近年来，随着深度学习三维神经网络的兴起，语义分割取得了快速进展，由此推动了对这种三维场景全面理解的研究兴趣日益增长。然而，迄今为止，关于户外移动测图数据的全景分割研究非常有限，且缺乏系统性的比较。本文试图填补这一空白。本文综述了构建全景分割流水线所需的基本模块及相关的文献，并搭建了一个模块化流水线，开展了全面系统的实验，以评估街景测图背景下全景分割的研究现状。作为副产品，我们还通过扩展NPM3D数据集以包含实例标签，首次公开了用于该任务的数据集。