论文标题
基于Kitti的基于激光底的圆锥分段的基准
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI
论文作者
论文摘要
Panoptic分割是最近引入的任务，可以共同处理语义细分和实例分割。在本文中，我们提出了Semantickitti的扩展，该扩展是一个大规模数据集，可为Kitti Odometry基准的所有序列提供密集的点语义标签，用于培训和评估基于激光的泛型分段。我们提供数据并讨论用时间一致的实例信息，即补充给定语义的实例信息所需的处理步骤，即补充语义标签的实例信息，并通过LIDAR点云的序列确定相同的实例。此外，我们提出了两个强大的基线，它们将基于最新的激光痛的语义分割方法与最先进的检测器结合了实例信息，并允许其他研究人员比较他们的方法相反。我们希望我们对Semantickitti具有强基础的扩展能够为基于激光的全盘细分创建新颖的算法与原始语义细分和语义场景完成任务一样多。数据，代码和使用隐藏测试集的在线评估将发布在http://semantic-kitti.org上。
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.
