  Detecting objects from LiDAR point clouds is an important component of
self-driving car technology as LiDAR provides high resolution spatial
information. Previous work on point-cloud 3D object detection has re-purposed
convolutional approaches from traditional camera imagery. In this work, we
present an object detection system called StarNet designed specifically to take
advantage of the sparse and 3D nature of point cloud data. StarNet is entirely
point-based, uses no global information, has data dependent anchors, and uses
sampling instead of learned region proposals. We demonstrate how this design
leads to competitive or superior performance on the large Waymo Open Dataset
and the KITTI detection dataset, as compared to convolutional baselines. In
particular, we show how our detector can outperform a competitive baseline on
Pedestrian detection on the Waymo Open Dataset by more than 7 absolute mAP
while being more computationally efficient. We show how our redesign---namely
using only local information and using sampling instead of learned
proposals---leads to a significantly more flexible and adaptable system: we
demonstrate how we can vary the computational cost of a single trained StarNet
without retraining, and how we can target proposals towards areas of interest
with priors and heuristics. Finally, we show how our design allows for
incorporating temporal context by using detections from previous frames to
target computation of the detector, which leads to further improvements in
performance without additional computational cost.
