论文标题
PC-RGNN：用于3D对象检测的点云完成和图形神经网络
PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection
论文作者
论文摘要
基于激光雷达的3D对象检测是自动驾驶的重要任务，而当前的方法却遭受了遥远和遮挡对象的稀疏和部分点云。在本文中，我们提出了一种新型的两阶段方法，即PC-RGNN，以两种特定的解决方案来应对此类挑战。一方面，我们引入了一个点云完成模块，以恢复保留原始结构的高质量建议和整个视图。另一方面，设计了图形神经网络模块，该模块通过局部全球注意机制以及基于多尺度图的上下文聚集来全面捕获点之间的关系，从而实质上增强了编码特征。 Kitti基准测试的广泛实验表明，所提出的方法通过显着的边缘优于先前的最先进基线，强调其有效性。
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.
