Ego3DT: Tracking Every 3D Object in Ego-centric Videos

• URL: http://arxiv.org/abs/2410.08530v1
• Date: Fri, 11 Oct 2024 05:02:31 GMT
• Title: Ego3DT: Tracking Every 3D Object in Ego-centric Videos
• Authors: Shengyu Hao, Wenhao Chai, Zhonghan Zhao, Meiqi Sun, Wendi Hu, Jieyang Zhou, Yixian Zhao, Qi Li, Yizhou Wang, Xi Li, Gaoang Wang,
• Abstract summary: This paper introduces a novel zero-shot approach for the 3D reconstruction and tracking of all objects from the ego-centric video. We present Ego3DT, a novel framework that initially identifies and extracts detection and segmentation information of objects within the ego environment. We have also innovated a dynamic hierarchical association mechanism for creating stable 3D tracking trajectories of objects in ego-centric videos.
• Score: 20.96550148331019
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing interest in embodied intelligence has brought ego-centric perspectives to contemporary research. One significant challenge within this realm is the accurate localization and tracking of objects in ego-centric videos, primarily due to the substantial variability in viewing angles. Addressing this issue, this paper introduces a novel zero-shot approach for the 3D reconstruction and tracking of all objects from the ego-centric video. We present Ego3DT, a novel framework that initially identifies and extracts detection and segmentation information of objects within the ego environment. Utilizing information from adjacent video frames, Ego3DT dynamically constructs a 3D scene of the ego view using a pre-trained 3D scene reconstruction model. Additionally, we have innovated a dynamic hierarchical association mechanism for creating stable 3D tracking trajectories of objects in ego-centric videos. Moreover, the efficacy of our approach is corroborated by extensive experiments on two newly compiled datasets, with 1.04x - 2.90x in HOTA, showcasing the robustness and accuracy of our method in diverse ego-centric scenarios.

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