# Uncertainty-aware Self-supervised 3D Data Association

arXiv: https://arxiv.org/abs/2008.08173 · HTML: https://mlsanity.com/paper/2008.08173/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM](https://mlsanity.com/paper/2210.16204/) (2022)
- [Representation Uncertainty in Self-Supervised Learning as Variational Inference](https://mlsanity.com/paper/2203.11437/) (2022)
- [Uncertainty-Aware AB3DMOT by Variational 3D Object Detection](https://mlsanity.com/paper/2302.05923/) (2023)
- [Variational Voxel Pseudo Image Tracking](https://mlsanity.com/paper/2302.05914/) (2023)
- [S3Track: Self-supervised Tracking with Soft Assignment Flow](https://mlsanity.com/paper/2305.09981/) (2023)
- [S3MOT: Monocular 3D Object Tracking with Selective State Space Model](https://mlsanity.com/paper/2504.18068/) (2025)

## Abstract (arXiv)

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.