  We present a new two-stage 3D object detection framework, named
sparse-to-dense 3D Object Detector (STD). The first stage is a bottom-up
proposal generation network that uses raw point cloud as input to generate
accurate proposals by seeding each point with a new spherical anchor. It
achieves a high recall with less computation compared with prior works. Then,
PointsPool is applied for generating proposal features by transforming their
interior point features from sparse expression to compact representation, which
saves even more computation time. In box prediction, which is the second stage,
we implement a parallel intersection-over-union (IoU) branch to increase
awareness of localization accuracy, resulting in further improved performance.
We conduct experiments on KITTI dataset, and evaluate our method in terms of 3D
object and Bird's Eye View (BEV) detection. Our method outperforms other
state-of-the-arts by a large margin, especially on the hard set, with inference
speed more than 10 FPS.
