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.

翻译：我们提出了一个改进版的3D天体探测点子RCNN,其中采用多处主干网处理点云的非统一密度,建议采用基于不确定性的取样政策处理不同点云分布差异,新模型的性能可以比基基点点RCNN在KITTI 校验集上的性能高约0.8 AP。此外,对每个集集散层采用单一比例分组的简化模型可以以较低的计算成本实现竞争性性能。