  3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and
approaches in recent years, especially those under the "tracking-by-detection"
paradigm. Despite their progress and usefulness, an in-depth analysis of their
strengths and weaknesses is not yet available. In this paper, we summarize
current 3D MOT methods into a unified framework by decomposing them into four
constituent parts: pre-processing of detection, association, motion model, and
life cycle management. We then ascribe the failure cases of existing algorithms
to each component and investigate them in detail. Based on the analyses, we
propose corresponding improvements which lead to a strong yet simple baseline:
SimpleTrack. Comprehensive experimental results on Waymo Open Dataset and
nuScenes demonstrate that our final method could achieve new state-of-the-art
results with minor modifications.
  Furthermore, we take additional steps and rethink whether current benchmarks
authentically reflect the ability of algorithms for real-world challenges. We
delve into the details of existing benchmarks and find some intriguing facts.
Finally, we analyze the distribution and causes of remaining failures in \name\
and propose future directions for 3D MOT. Our code is available at
https://github.com/TuSimple/SimpleTrack.
