This paper introduces MCTrack, a new 3D multi-object tracking method that achieves state-of-the-art (SOTA) performance across KITTI, nuScenes, and Waymo datasets. Addressing the gap in existing tracking paradigms, which often perform well on specific datasets but lack generalizability, MCTrack offers a unified solution. Additionally, we have standardized the format of perceptual results across various datasets, termed BaseVersion, facilitating researchers in the field of multi-object tracking (MOT) to concentrate on the core algorithmic development without the undue burden of data preprocessing. Finally, recognizing the limitations of current evaluation metrics, we propose a novel set that assesses motion information output, such as velocity and acceleration, crucial for downstream tasks. The source codes of the proposed method are available at this link: https://github.com/megvii-research/MCTrack}{https://github.com/megvii-research/MCTrack

本研究提出了MCTrack，一种新的3D多目标跟踪方法，解决了现有跟踪模式在不同数据集泛化能力不足的问题。MCTrack提供了统一的解决方案，并统一了各数据集的感知结果格式，帮助多目标跟踪领域的研究者集中在核心算法开发上。此外，我们提出了一套新的评估指标，以评估运动信息输出，这对下游任务至关重要。

MCTrack：一种统一的3D多目标跟踪框架用于自动驾驶