  To reduce the expensive labor cost for manual labeling autonomous driving
datasets, an alternative is to automatically label the datasets using an
offline perception system. However, objects might be temporally occluded. Such
occlusion scenarios in the datasets are common yet underexplored in offline
auto labeling. In this work, we propose an offline tracking model that focuses
on occluded object tracks. It leverages the concept of object permanence which
means objects continue to exist even if they are not observed anymore. The
model contains three parts: a standard online tracker, a re-identification
(Re-ID) module that associates tracklets before and after occlusion, and a
track completion module that completes the fragmented tracks. The Re-ID module
and the track completion module use the vectorized map as one of the inputs to
refine the tracking results with occlusion. The model can effectively recover
the occluded object trajectories. It achieves state-of-the-art performance in
3D multi-object tracking by significantly improving the original online
tracking result, showing its potential to be applied in offline auto labeling
as a useful plugin to improve tracking by recovering occlusions.
