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Panoptic scene understanding and tracking of dynamic agents are essential forrobots and automated vehicles to navigate in urban environments. As LiDARsprovide 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 havea limited number of dynamic object instances which hinders both learning ofthese tasks as well as credible benchmarking of the developed methods. In thispaper, we introduce the large-scale Panoptic nuScenes benchmark dataset thatextends our popular nuScenes dataset with point-wise groundtruth annotationsfor semantic segmentation, panoptic segmentation, and panoptic tracking tasks.To facilitate comparison, we provide several strong baselines for each of thesetasks on our proposed dataset. Moreover, we analyze the drawbacks of theexisting metrics for panoptic tracking and propose the novel instance-centricPAT metric that addresses the concerns. We present exhaustive experiments thatdemonstrate the utility of Panoptic nuScenes compared to existing datasets andmake the online evaluation server available at nuScenes.org. We believe thatthis extension will accelerate the research of novel methods for sceneunderstanding of dynamic urban environments.

Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking