In this paper, we propose a graph neural network to detect objects from a
LiDAR point cloud. Towards this end, we encode the point cloud efficiently in a
fixed radius near-neighbors graph. We design a graph neural network, named
Point-GNN, to predict the category and shape of the object that each vertex in
the graph belongs to. In Point-GNN, we propose an auto-registration mechanism
to reduce translation variance, and also design a box merging and scoring
operation to combine detections from multiple vertices accurately. Our
experiments on the KITTI benchmark show the proposed approach achieves leading
accuracy using the point cloud alone and can even surpass fusion-based
algorithms. Our results demonstrate the potential of using the graph neural
network as a new approach for 3D object detection. The code is available
this https URL

本研究提出了一种基于图神经网络的 LiDAR 点云对象检测方法，采用自动注册机制降低了平移方差，并结合盒子合并和计分运算从多个数据点准确地组合检测结果，在 KITTI 基准测试中，该方法仅使用点云即可实现领先的准确性，甚至胜过融合算法，这显示了使用图神经网络作为三维对象检测的新方法的潜力。