  Existing offboard 3D detectors always follow a modular pipeline design to
take advantage of unlimited sequential point clouds. We have found that the
full potential of offboard 3D detectors is not explored mainly due to two
reasons: (1) the onboard multi-object tracker cannot generate sufficient
complete object trajectories, and (2) the motion state of objects poses an
inevitable challenge for the object-centric refining stage in leveraging the
long-term temporal context representation. To tackle these problems, we propose
a novel paradigm of offboard 3D object detection, named DetZero. Concretely, an
offline tracker coupled with a multi-frame detector is proposed to focus on the
completeness of generated object tracks. An attention-mechanism refining module
is proposed to strengthen contextual information interaction across long-term
sequential point clouds for object refining with decomposed regression methods.
Extensive experiments on Waymo Open Dataset show our DetZero outperforms all
state-of-the-art onboard and offboard 3D detection methods. Notably, DetZero
ranks 1st place on Waymo 3D object detection leaderboard with 85.15 mAPH (L2)
detection performance. Further experiments validate the application of taking
the place of human labels with such high-quality results. Our empirical study
leads to rethinking conventions and interesting findings that can guide future
research on offboard 3D object detection.
