  Multi-object tracking (MOT) is an integral part of any autonomous driving
pipelines because itproduces trajectories which has been taken by other moving
objects in the scene and helps predicttheir future motion. Thanks to the recent
advances in 3D object detection enabled by deep learning,track-by-detection has
become the dominant paradigm in 3D MOT. In this paradigm, a MOT systemis
essentially made of an object detector and a data association algorithm which
establishes track-to-detection correspondence. While 3D object detection has
been actively researched, associationalgorithms for 3D MOT seem to settle at a
bipartie matching formulated as a linear assignmentproblem (LAP) and solved by
the Hungarian algorithm. In this paper, we adapt a two-stage dataassociation
method which was successful in image-based tracking to the 3D setting, thus
providingan alternative for data association for 3D MOT. Our method outperforms
the baseline using one-stagebipartie matching for data association by achieving
0.587 AMOTA in NuScenes validation set.
