A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene completion for predictive reasoning on the semantics and occluded regions; and (3) panoptic segmentation combining point-wise classification and assigning individual instance identities to separate objects of the same class. In this article, we provide details on our dataset showing an unprecedented number of fully annotated point cloud sequences, more information on our labeling process to efficiently annotate such a vast amount of point clouds, and lessons learned in this process. The dataset and resources are available at http://www.semantic-kitti.org.

利用所有可用的传感器模式进行整体语义场景理解是掌握复杂日常交通中自动驾驶的核心能力。为此，我们介绍了SemanticKITTI该数据集提供了KITTI里程表基准测试的Velodyne HDL-64E点云的逐点语义注释。与数据一起，我们还发布了三个语义语义理解基准任务，涵盖了语义语义理解的不同方面：（1）使用单点云或多点云作为输入的按点分类的语义分割；（2）语义场景完成，用于对语义和遮挡区域进行预测性推理；（3）全景分割结合逐点分类，并将个体实例标识分配给同一类别的单独对象。在本文中，我们将提供有关数据集的详细信息，其中显示了前所未有的数量的完全注释的点云序列，有关标记过程的更多信息，可以有效地注释大量的点云，和在此过程中汲取的经验教训。数据集和资源可在http://www.semantic-kitti.org上获得。