3D Multi-Object Tracking (MOT) has achieved tremendous achievement thanks to
the rapid development of 3D object detection and 2D MOT. Recent advanced works
generally employ a series of object attributes, e.g., position, size, velocity,
and appearance, to provide the clues for the association in 3D MOT. However,
these cues may not be reliable due to some visual noise, such as occlusion and
blur, leading to tracking performance bottleneck. To reveal the dilemma, we
conduct extensive empirical analysis to expose the key bottleneck of each clue
and how they correlate with each other. The analysis results motivate us to
efficiently absorb the merits among all cues, and adaptively produce an optimal
tacking manner. Specifically, we present Location and Velocity Quality
Learning, which efficiently guides the network to estimate the quality of
predicted object attributes. Based on these quality estimations, we propose a
quality-aware object association (QOA) strategy to leverage the quality score
as an important reference factor for achieving robust association. Despite its
simplicity, extensive experiments indicate that the proposed strategy
significantly boosts tracking performance by 2.2% AMOTA and our method
outperforms all existing state-of-the-art works on nuScenes by a large margin.
Moreover, QTrack achieves 48.0% and 51.1% AMOTA tracking performance on the
nuScenes validation and test sets, which significantly reduces the performance
gap between pure camera and LiDAR based trackers.

本文基于大量实证分析，提出了一种基于物体属性质量评估的分层联结策略，可有效提高三维多目标跟踪的性能表现。实验结果表明，该策略使得跟踪性能提高了 2.2% AMOTA，并在 nuScenes 数据集上表现出领先于现有最新技术的优异结果。