We propose a graph neural network(GNN) based method to incorporate scene context for the semantic segmentation of 3D LiDAR data. The problem is defined as building a graph to represent the topology of a center segment with its neighborhoods, then inferring the segment label. The node of graph is generated from the segment on range image, which is suitable for both sparse and dense point cloud. Edge weights that evaluate the correlations of center node and its neighborhoods are automatically encoded by a neural network, therefore the number of neighbor nodes is no longer a sensitive parameter. A system consists of segment generation, graph building, edge weight estimation, node updating, and node prediction is designed. Quantitative evaluation on a dataset of dynamic scene shows that our method has better performance than unary CNN with 8% improvement, as well as normal GNN with 17% improvement.

中文翻译：

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动态场景中基于场景上下文的 3D LiDAR 数据语义分割

我们提出了一种基于图神经网络（GNN）的方法，以结合场景上下文来对 3D LiDAR 数据进行语义分割。该问题被定义为构建一个图来表示中心线段及其邻域的拓扑结构，然后推断线段标签。图的节点由距离图像上的线段生成，适用于稀疏点云和密集点云。评估中心节点与其邻域相关性的边权重由神经网络自动编码，因此相邻节点的数量不再是敏感参数。设计了一个由段生成、图构建、边权重估计、节点更新和节点预测组成的系统。