Uncertainty-aware self-supervised 3D data association

Jianren Wang, Siddharth Ancha, Yi-Ting Chen, David Held

研究成果: Conference contribution同行評審

6 引文 斯高帕斯(Scopus)

摘要

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/.

原文English
主出版物標題2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面8125-8132
頁數8
ISBN(電子)9781728162126
DOIs
出版狀態Published - 24 10月 2020
事件2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States
持續時間: 24 10月 202024 1月 2021

出版系列

名字IEEE International Conference on Intelligent Robots and Systems
ISSN(列印)2153-0858
ISSN(電子)2153-0866

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
國家/地區United States
城市Las Vegas
期間24/10/2024/01/21

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