  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. That dataset and our source code are
publicly available. We discuss which adaptations are need to adapt current
panoptic segmentation methods to outdoor scenes and large objects. Our study
finds that for mobile mapping data, KPConv performs best but is slower, while
PointNet++ is fastest but performs significantly worse. Sparse CNNs are in
between. Regardless of the backbone, Instance segmentation by clustering
embedding features is better than using shifted coordinates.
