A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI
This work addresses the need for standardized evaluation in LiDAR panoptic segmentation for autonomous driving research, but it is incremental as it builds upon existing datasets and methods.
The authors tackled the problem of LiDAR-based panoptic segmentation by extending the SemanticKITTI dataset with temporally consistent instance annotations and providing strong baselines, resulting in a new benchmark that includes data, code, and an online evaluation system.
Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly. In this paper, we present an extension of SemanticKITTI, which is a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Benchmark, for training and evaluation of laser-based panoptic segmentation. We provide the data and discuss the processing steps needed to enrich a given semantic annotation with temporally consistent instance information, i.e., instance information that supplements the semantic labels and identifies the same instance over sequences of LiDAR point clouds. Additionally, we present two strong baselines that combine state-of-the-art LiDAR-based semantic segmentation approaches with a state-of-the-art detector enriching the segmentation with instance information and that allow other researchers to compare their approaches against. We hope that our extension of SemanticKITTI with strong baselines enables the creation of novel algorithms for LiDAR-based panoptic segmentation as much as it has for the original semantic segmentation and semantic scene completion tasks. Data, code, and an online evaluation using a hidden test set will be published on http://semantic-kitti.org.