论文标题
动态场景中3D LIDAR数据的基于场景上下文的语义细分
Scene Context Based Semantic Segmentation for 3D LiDAR Data in Dynamic Scene
论文作者
论文摘要
我们提出了一种基于图形神经网络（GNN）的方法，以合并3D LIDAR数据的语义分割的场景上下文。该问题被定义为构建图形，以代表中心段的彼此及其社区的拓扑，然后推断该段标签。图的节点是从范围图像上的段中生成的，该节点适用于稀疏和致密点云。评估中心节点及其邻域相关性的边缘权重自动由神经网络编码，因此邻居节点的数量不再是敏感参数。一个系统由段生成，图形构建，边缘权重估计，节点更新和节点预测组成。动态场景数据集上的定量评估表明，我们的方法的性能要比Unary CNN更好，并提高了8％，并且正常GNN，并提高了17％。
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.
