  In the past few years we have seen great advances in object perception
(particularly in 4D space-time dimensions) thanks to deep learning methods.
However, they typically rely on large amounts of high-quality labels to achieve
good performance, which often require time-consuming and expensive work by
human annotators. To address this we propose an automatic annotation pipeline
that generates accurate object trajectories in 3D space (i.e., 4D labels) from
LiDAR point clouds. The key idea is to decompose the 4D object label into two
parts: the object size in 3D that's fixed through time for rigid objects, and
the motion path describing the evolution of the object's pose through time.
Instead of generating a series of labels in one shot, we adopt an iterative
refinement process where online generated object detections are tracked through
time as the initialization. Given the cheap but noisy input, our model produces
higher quality 4D labels by re-estimating the object size and smoothing the
motion path, where the improvement is achieved by exploiting aggregated
observations and motion cues over the entire trajectory. We validate the
proposed method on a large-scale driving dataset and show a 25% reduction of
human annotation efforts. We also showcase the benefits of our approach in the
annotator-in-the-loop setting.
