论文标题
在点云中用于3D对象检测的密度感知的pointrcnn
A Density-Aware PointRCNN for 3D Object Detection in Point Clouds
论文作者
论文摘要
我们提出了用于3D对象检测的Pointrcnn的改进版本，其中采用了多支球骨干网络来处理点云的非均匀密度。提出了基于不确定性的抽样策略来处理不同点云的分布差异。与Kitti Val集合上的基线POINTRCNN相比，新模型的性能高约0.8 AP。此外，使用单个设定层的单个比例分组的简化模型可以以较少的计算成本来实现竞争性能。
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.
