  3D object trackers usually require training on large amounts of annotated
data that is expensive and time-consuming to collect. Instead, we propose
leveraging vast unlabeled datasets by self-supervised metric learning of 3D
object trackers, with a focus on data association. Large scale annotations for
unlabeled data are cheaply obtained by automatic object detection and
association across frames. We show how these self-supervised annotations can be
used in a principled manner to learn point-cloud embeddings that are effective
for 3D tracking. We estimate and incorporate uncertainty in self-supervised
tracking to learn more robust embeddings, without needing any labeled data. We
design embeddings to differentiate objects across frames, and learn them using
uncertainty-aware self-supervised training. Finally, we demonstrate their
ability to perform accurate data association across frames, towards effective
and accurate 3D tracking. Project videos and code are at
https://jianrenw.github.io/Self-Supervised-3D-Data-Association.
