  We present an improved version of PointRCNN for 3D object detection, in which
a multi-branch backbone network is adopted to handle the non-uniform density of
point clouds. An uncertainty-based sampling policy is proposed to deal with the
distribution differences of different point clouds. The new model can achieve
about 0.8 AP higher performance than the baseline PointRCNN on KITTI val set.
In addition, a simplified model using a single scale grouping for each
set-abstraction layer can achieve competitive performance with less
computational cost.
