  We present a simple and flexible object detection framework optimized for
autonomous driving. Building on the observation that point clouds in this
application are extremely sparse, we propose a practical pillar-based approach
to fix the imbalance issue caused by anchors. In particular, our algorithm
incorporates a cylindrical projection into multi-view feature learning,
predicts bounding box parameters per pillar rather than per point or per
anchor, and includes an aligned pillar-to-point projection module to improve
the final prediction. Our anchor-free approach avoids hyperparameter search
associated with past methods, simplifying 3D object detection while
significantly improving upon state-of-the-art.
