  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.
