Panoptic scene understanding and tracking of dynamic agents are essential for
robots and automated vehicles to navigate in urban environments. As LiDARs
provide accurate illumination-independent geometric depictions of the scene,
performing these tasks using LiDAR point clouds provides reliable predictions.
However, existing datasets lack diversity in the type of urban scenes and have
a limited number of dynamic object instances which hinders both learning of
these tasks as well as credible benchmarking of the developed methods. In this
paper, we introduce the large-scale Panoptic nuScenes benchmark dataset that
extends our popular nuScenes dataset with point-wise groundtruth annotations
for semantic segmentation, panoptic segmentation, and panoptic tracking tasks.
To facilitate comparison, we provide several strong baselines for each of these
tasks on our proposed dataset. Moreover, we analyze the drawbacks of the
existing metrics for panoptic tracking and propose the novel instance-centric
PAT metric that addresses the concerns. We present exhaustive experiments that
demonstrate the utility of Panoptic nuScenes compared to existing datasets and
make the online evaluation server available at nuScenes.org. We believe that
this extension will accelerate the research of novel methods for scene
understanding of dynamic urban environments.

介绍了扩展了 nuScenes 数据集的大规模全景 nuScenes 基准数据集，以便对动态城市环境的全景场景理解方法进行研究和评估，同时提出了各种分割和跟踪任务的强基线方法，并提出了面向实例的新型 PAT 指标，解决了现有指标的局限性。