  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.
