  While current 3D object recognition research mostly focuses on the real-time,
onboard scenario, there are many offboard use cases of perception that are
largely under-explored, such as using machines to automatically generate
high-quality 3D labels. Existing 3D object detectors fail to satisfy the
high-quality requirement for offboard uses due to the limited input and speed
constraints. In this paper, we propose a novel offboard 3D object detection
pipeline using point cloud sequence data. Observing that different frames
capture complementary views of objects, we design the offboard detector to make
use of the temporal points through both multi-frame object detection and novel
object-centric refinement models. Evaluated on the Waymo Open Dataset, our
pipeline named 3D Auto Labeling shows significant gains compared to the
state-of-the-art onboard detectors and our offboard baselines. Its performance
is even on par with human labels verified through a human label study. Further
experiments demonstrate the application of auto labels for semi-supervised
learning and provide extensive analysis to validate various design choices.
