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In this paper, we propose a graph neural network to detect objects from aLiDAR point cloud. Towards this end, we encode the point cloud efficiently in afixed radius near-neighbors graph. We design a graph neural network, namedPoint-GNN, to predict the category and shape of the object that each vertex inthe graph belongs to. In Point-GNN, we propose an auto-registration mechanismto reduce translation variance, and also design a box merging and scoringoperation to combine detections from multiple vertices accurately. Ourexperiments on the KITTI benchmark show the proposed approach achieves leadingaccuracy using the point cloud alone and can even surpass fusion-basedalgorithms. Our results demonstrate the potential of using the graph neuralnetwork as a new approach for 3D object detection. The code is availablehttps://github.com/WeijingShi/Point-GNN.

Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud