A Graph Attention Network for Object Detection from Raw LiDAR Data

In this work, we present an attention based feature aggregation technique in graph neural networks (GNN) for detecting objects in LiDAR scan. We first employ a distance-aware downsampling scheme that not only enhances the algorithmic performance but also retains maximum geometric features of objects even if they lie far from the sensor. Our graph attention formulation uses novel neighborhood aggregation strategies through per node masked attention by combining local and self features. The experiments on KITTI dataset show that the proposed method yields comparable results for 3D object detection under GNN models category.