  LiDAR-based 3D object detection is an important task for autonomous driving
and current approaches suffer from sparse and partial point clouds of distant
and occluded objects. In this paper, we propose a novel two-stage approach,
namely PC-RGNN, dealing with such challenges by two specific solutions. On the
one hand, we introduce a point cloud completion module to recover high-quality
proposals of dense points and entire views with original structures preserved.
On the other hand, a graph neural network module is designed, which
comprehensively captures relations among points through a local-global
attention mechanism as well as multi-scale graph based context aggregation,
substantially strengthening encoded features. Extensive experiments on the
KITTI benchmark show that the proposed approach outperforms the previous
state-of-the-art baselines by remarkable margins, highlighting its
effectiveness.
