A Density-Aware PointRCNN for 3D Object Detection in Point Clouds [article]

Jie Li, Yu Hu
2021 arXiv   pre-print
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
more » ... ompetitive performance with less computational cost.
arXiv:2009.05307v2 fatcat:tysspeuk3nfdtiq5didlc6wx7q