Abstract
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
https://github.com/WeijingShi/Point-GNN.
Users
Please
log in to take part in the discussion (add own reviews or comments).