The KITTI Vision Benchmark Suite

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

TBD

@inproceedings{wang2025mctrack,
title={Mctrack: A unified 3d multi-object
tracking framework for autonomous driving},
author={Wang, Xiyang and Qi, Shouzheng and Zhao,
Jieyou and Zhou, Hangning and Zhang, Siyu and
Wang, Guoan and Tu, Kai and Guo, Songlin and Zhao,
Jianbo and Li, Jian and others},
booktitle={2025 IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS)},
pages={4551--4558},
year={2025},
organization={IEEE}
}