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
this https URL

本研究提出利用自我监督度量学习的方式，以数据关联为重点，从大量未标注数据集中学习 3D 物体跟踪器。自我监督注释可以被用于有原则的学习点云嵌入，实现数据关联和 3D 跟踪。估计并结合跟踪的不确定度，设计嵌入以识别不同帧间的物体，通过未标注数据的训练，实现了更强大的跟踪效果。最终得到的跟踪器能够在帧间执行准确的数据关联，在 3D 跟踪中表现出色。