Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy.

翻译：全景分割是语义分割与实例分割的结合：将3D点云中的点分配至语义类别，并将其划分为不同的对象实例。该方法在城市测绘到森林管理等室外场景理解中具有诸多显著应用。现有方法难以分割同一语义类别的邻近实例（如相邻街道设施或邻近树木），这限制了其在依赖对象实例的清单或管理类应用中的实用性。本研究探索了全景分割流程中将点聚类为对象实例的关键步骤，旨在缓解这一瓶颈问题。我们提出一种精心设计的聚类策略，该策略利用多种类型的习得点嵌入，显著改进了实例分割性能。在NPM3D城市移动测绘数据集和FOR-instance森林数据集上的实验证明了所提策略的有效性和通用性。