  3D multi-object tracking is an important component in robotic perception
systems such as self-driving vehicles. Recent work follows a
tracking-by-detection pipeline, which aims to match past tracklets with
detections in the current frame. To avoid matching with false positive
detections, prior work filters out detections with low confidence scores via a
threshold. However, finding a proper threshold is non-trivial, which requires
extensive manual search via ablation study. Also, this threshold is sensitive
to many factors such as target object category so we need to re-search the
threshold if these factors change. To ease this process, we propose to
automatically select high-quality detections and remove the efforts needed for
manual threshold search. Also, prior work often uses a single threshold per
data sequence, which is sub-optimal in particular frames or for certain
objects. Instead, we dynamically search threshold per frame or per object to
further boost performance. Through experiments on KITTI and nuScenes, our
method can filter out $45.7\%$ false positives while maintaining the recall,
achieving new S.O.T.A. performance and removing the need for manually threshold
tuning.
