Offline-Poly: A Polyhedral Framework For Offline 3D Multi-Object Tracking
Xiaoyu Li, Yitao Wu, Xian Wu, Haolin Zhuo, Lijun Zhao, Lining Sun

TL;DR
Offline-Poly introduces a flexible, tracking-centric framework for offline 3D multi-object tracking that leverages global optimization and future observability to improve accuracy and robustness beyond existing online-based methods.
Contribution
The paper proposes Offline-Poly, a novel offline 3D MOT framework based on a standardized Tracking-by-Tracking paradigm that decouples the tracker from specific detectors and enhances tracklet refinement.
Findings
Achieves state-of-the-art performance on nuScenes with 77.6% AMOTA.
Attains leading results on KITTI with 83.00% HOTA.
Demonstrates high flexibility and generalizability across different datasets.
Abstract
Offline 3D multi-object tracking (MOT) is a critical component of the 4D auto-labeling (4DAL) process. It enhances pseudo-labels generated by high-performance detectors through the incorporation of temporal context. However, existing offline 3D MOT approaches are direct extensions of online frameworks and fail to fully exploit the advantages of offline setting. Moreover, these methods often depend on fixed upstream and customized architectures, limiting their adaptability. To address these limitations, we propose Offline-Poly, a general offline 3D MOT method based on a tracking-centric design. We introduce a standardized paradigm termed Tracking-by-Tracking (TBT), which operates exclusively on arbitrary off-the-shelf tracking outputs and produces offline-refined tracklets. This formulation decouples offline tracker from specific upstream detectors or trackers. Under the TBT paradigm,…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Target Tracking and Data Fusion in Sensor Networks · Advanced Technologies in Various Fields
