  A robust and accurate 3D detection system is an integral part of autonomous
vehicles. Traditionally, a majority of 3D object detection algorithms focus on
processing 3D point clouds using voxel grids or bird's eye view (BEV). Recent
works, however, demonstrate the utilization of the graph neural network (GNN)
as a promising approach to 3D object detection. In this work, we propose an
attention based feature aggregation technique in GNN for detecting objects in
LiDAR scan. We first employ a distance-aware down-sampling scheme that not only
enhances the algorithmic performance but also retains maximum geometric
features of objects even if they lie far from the sensor. In each layer of the
GNN, apart from the linear transformation which maps the per node input
features to the corresponding higher level features, a per node masked
attention by specifying different weights to different nodes in its first ring
neighborhood is also performed. The masked attention implicitly accounts for
the underlying neighborhood graph structure of every node and also eliminates
the need of costly matrix operations thereby improving the detection accuracy
without compromising the performance. The experiments on KITTI dataset show
that our method yields comparable results for 3D object detection.
