In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task, using the metric and annotations, we set multiple baselines by merging results of existing state-of-the-art methods for panoptic segmentation and part segmentation. Finally, we conduct several experiments that evaluate the importance of the different levels of abstraction in this single task.

翻译：在这项工作中,我们引入了全视全光截面(PPS)的新场景理解任务,目的是了解多层抽象的场景,统一现场剖面和部分分割的任务。对于这项新任务,我们对两种常用数据集:城市景和帕斯卡尔 VOC提供了一致的说明。此外,我们提出了一个单一的衡量标准来评估PPS,称为“全光截面质量(PartPQ ) ” 。对于这项新任务,我们使用度量和说明,通过合并现有全光截面分割和部分分割最新方法的结果,确定了多个基线。最后,我们进行了几项实验,评估了这一单一任务中不同程度的抽象的重要性。