Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
This work provides a large-scale benchmark for researchers in robotics and autonomous vehicles to develop and evaluate methods for LiDAR-based panoptic segmentation and tracking in urban scenes.
The authors introduced the Panoptic nuScenes dataset to address the lack of diversity and limited instances in existing LiDAR datasets for panoptic segmentation and tracking, providing point-wise annotations and strong baselines that improved benchmarking and research in dynamic urban environments.
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.