MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud
  Sequences
Yingwei Li Charles R. Qi Yin Zhou Chenxi Liu Dragomir Anguelov
Occluded and long-range objects are ubiquitous and challenging for 3D object
detection. Point cloud sequence data provide unique opportunities to improve
such cases, as an occluded or distant object can be observed from different
viewpoints or gets better visibility over time. However, the efficiency and
effectiveness in encoding long-term sequence data can still be improved. In
this work, we propose MoDAR, using motion forecasting outputs as a type of
virtual modality, to augment LiDAR point clouds. The MoDAR modality propagates
object information from temporal contexts to a target frame, represented as a
set of virtual points, one for each object from a waypoint on a forecasted
trajectory. A fused point cloud of both raw sensor points and the virtual
points can then be fed to any off-the-shelf point-cloud based 3D object
detector. Evaluated on the Waymo Open Dataset, our method significantly
improves prior art detectors by using motion forecasting from extra-long
sequences (e.g. 18 seconds), achieving new state of the arts, while not adding
much computation overhead.