Panoptic segmentation has recently unified semantic and instance
segmentation, previously addressed separately, thus taking a step further
towards creating more comprehensive and efficient perception systems. In this
paper, we present Panoster, a novel proposal-free panoptic segmentation method
for LiDAR point clouds. Unlike previous approaches relying on several steps to
group pixels or points into objects, Panoster proposes a simplified framework
incorporating a learning-based clustering solution to identify instances. At
inference time, this acts as a class-agnostic segmentation, allowing Panoster
to be fast, while outperforming prior methods in terms of accuracy. Without any
post-processing, Panoster reached state-of-the-art results among published
approaches on the challenging SemanticKITTI benchmark, and further increased
its lead by exploiting heuristic techniques. Additionally, we showcase how our
method can be flexibly and effectively applied on diverse existing semantic
architectures to deliver panoptic predictions.

本文提出了一种名为 Panoster 的新型无需分类器的全景分割方法，通过学习聚类解决方案来生成类不可知分割，不仅快速而且能够在 SemanticKITTI 基准测试中超过现有方法，并展示了如何在现有的语义架构中灵活地有效地应用我们的方法来提供全景预测。