论文标题
不确定性感知的自我监督3D数据协会
Uncertainty-aware Self-supervised 3D Data Association
论文作者
论文摘要
3D对象跟踪器通常需要对大量注释的数据进行培训，这些数据昂贵且耗时。取而代之的是，我们建议通过对3D对象跟踪器的自我监督的度量学习来利用大量未标记的数据集，重点关注数据关联。无标记数据的大规模注释是通过跨帧的自动对象检测和关联来廉价获得的。我们展示了如何以原则性的方式使用这些自我监督注释，以学习有效的3D跟踪的点云嵌入。我们估计并将不确定性纳入自我监督的跟踪中，以了解更多可靠的嵌入，而无需任何标记的数据。我们设计嵌入以区分跨帧的物体，并使用不确定性意识的自我监督训练来学习它们。最后，我们证明了他们在跨框架上执行准确数据关联的能力，朝着有效而准确的3D跟踪。项目视频和代码在https://jianrenw.github.io/self-supervised-3d-data-association。
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.
