  This paper aims for high-performance offline LiDAR-based 3D object detection.
We first observe that experienced human annotators annotate objects from a
track-centric perspective. They first label the objects with clear shapes in a
track, and then leverage the temporal coherence to infer the annotations of
obscure objects. Drawing inspiration from this, we propose a high-performance
offline detector in a track-centric perspective instead of the conventional
object-centric perspective. Our method features a bidirectional tracking module
and a track-centric learning module. Such a design allows our detector to infer
and refine a complete track once the object is detected at a certain moment. We
refer to this characteristic as "onCe detecTed, neveR Lost" and name the
proposed system CTRL. Extensive experiments demonstrate the remarkable
performance of our method, surpassing the human-level annotating accuracy and
the previous state-of-the-art methods in the highly competitive Waymo Open
Dataset without model ensemble. The code will be made publicly available at
https://github.com/tusen-ai/SST.
