  Densely annotating LiDAR point clouds remains too expensive and
time-consuming to keep up with the ever growing volume of data. While current
literature focuses on fully-supervised performance, developing efficient
methods that take advantage of realistic weak supervision have yet to be
explored. In this paper, we propose using scribbles to annotate LiDAR point
clouds and release ScribbleKITTI, the first scribble-annotated dataset for
LiDAR semantic segmentation. Furthermore, we present a pipeline to reduce the
performance gap that arises when using such weak annotations. Our pipeline
comprises of three stand-alone contributions that can be combined with any
LiDAR semantic segmentation model to achieve up to 95.7% of the
fully-supervised performance while using only 8% labeled points. Our scribble
annotations and code are available at github.com/ouenal/scribblekitti.
