论文标题
学会从没有标签的LiDAR扫描中检测移动对象
Learning to Detect Mobile Objects from LiDAR Scans Without Labels
论文作者
论文摘要
当前用于自动驾驶的3D对象检测器几乎完全接受了人类通知数据的训练。尽管具有高质量，但此类数据的产生却很费力且昂贵，将它们限制在一些特定的位置和对象类型中。本文完全基于未标记的数据提出了一种替代方法，该方法几乎可以廉价地收集到地球上任何地方。我们的方法利用了几种简单的常识启发式方法来创建一组初始的近似种子标签。例如，相关的交通参与者通常不会在同一路线的多个遍历中持续存在，不要飞行，并且永远不会落在地面上。我们证明，这些种子标签非常有效，可以通过反复训练而没有单个人体注释的标签来引导精确的检测器。
Current 3D object detectors for autonomous driving are almost entirely trained on human-annotated data. Although of high quality, the generation of such data is laborious and costly, restricting them to a few specific locations and object types. This paper proposes an alternative approach entirely based on unlabeled data, which can be collected cheaply and in abundance almost everywhere on earth. Our approach leverages several simple common sense heuristics to create an initial set of approximate seed labels. For example, relevant traffic participants are generally not persistent across multiple traversals of the same route, do not fly, and are never under ground. We demonstrate that these seed labels are highly effective to bootstrap a surprisingly accurate detector through repeated self-training without a single human annotated label.
