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.

本研究提出一种新的两阶段方法，其中包括点云完成模块以恢复高质量的点密集提案和整个视图，同时设计了图神经网络模块，实现了全局本地关联机制和多尺度图形的上下文聚合，显著强化编码特征，实验表明，所提出的方法在 KITTI 基准测试中优于之前的基线算法，突出了其有效性。