论文标题
点-gnn：用于点云中3D对象检测的图形神经网络
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
论文作者
论文摘要
在本文中，我们提出了一个图形神经网络来检测激光点云的对象。为此，我们在接近邻骨的固定半径图中有效地编码点云。我们设计了一个名为Point-GNN的图形神经网络，以预测图表中每个顶点所属对象的类别和形状。在Point-gnn中，我们提出了一种自动注册机制，以减少翻译差异，并设计一个盒子合并和评分操作，以合并来自多个顶点的检测。我们在KITTI基准测试的实验表明，仅使用点云就可以实现所提出的方法，甚至可以超过基于融合的算法。我们的结果表明，将图形神经网络用作3D对象检测的新方法的潜力。该代码可用https://github.com/weijingshi/point-gnn。
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.
