Annotating lidar point clouds for autonomous driving is a notoriously expensive and time-consuming task. In this work, we show that the quality of recent self-supervised lidar scan representations allows a great reduction of the annotation cost. Our method has two main steps. First, we show that self-supervised representations allow a simple and direct selection of highly informative lidar scans to annotate: training a network on these selected scans leads to much better results than a random selection of scans and, more interestingly, to results on par with selections made by SOTA active learning methods. In a second step, we leverage the same self-supervised representations to cluster points in our selected scans. Asking the annotator to classify each cluster, with a single click per cluster, then permits us to close the gap with fully-annotated training sets, while only requiring one thousandth of the point labels.

翻译：自动驾驶中激光雷达点云的标注是一项众所周知昂贵且耗时的任务。在本工作中，我们证明了近期自监督激光雷达扫描表示的质量能够大幅降低标注成本。我们的方法包含两个主要步骤。首先，我们表明自监督表示允许对信息量丰富的激光雷达扫描进行简单直接的选择以进行标注：在这些选定扫描上训练网络所获得的结果，不仅远优于随机选择的扫描，更有趣的是，其性能与通过最先进主动学习方法选择的扫描结果相当。在第二步中，我们利用相同的自监督表示对选定扫描中的点进行聚类。通过要求标注者仅需对每个聚类进行一次点击分类，我们得以弥合与全标注训练集之间的性能差距，同时仅需千分之一的点标注量。