基于图模型与注意力机制的室外场景点云分割模型

Outdoor scene point cloud segmentation model based on graph model and attention mechanism

针对在多对象且空间拓扑关系复杂的室外场景环境中相似地类区分难的问题,提出一种结合图模型与注意力机制模块的A-Edge-SPG(Attention-EdgeConv SuperPoint Graph)图神经网络。首先,利用图割和几何特征结合的方法对超点进行分割;其次,在超点内部构造局部邻接图,从而在捕获场景中点云的上下文信息的同时利用注意力机制模块凸显关键信息;最后,构建超点图(SPG)模型,并采用门控循环单元(GRU)聚合超点和超边特征,实现对不同地类点云间的精确分割。在Semantic3D数据集上对A-Edge-SPG模型和SPG-Net(SPG neural Network)模型的语义分割效果进行比较分析。实验结果表明,相较于SPG模型,A-Edge-SPG模型在总体分割精度(OA)、平均交并比(mIoU)和平均精度均值(mAA)上分别提升了1.8、5.1和2.8个百分点,并且在高植被、矮植被等相似地类的分割精度上取得了明显的提升,改善了相似地类间语义分割的效果。

Aiming at the problem that it is difficult to distinguish similar land types in outdoor scenes with multiple objects and complex spatial topological relationships,an A-Edge-SPG(Attention-EdgeConv SuperPoint Graph)graph neural network combining graph model and attention mechanism module was proposed.Firstly,the superpoints were segmented by the combination of graph cut and geometric features.Secondly,the local adjacency graph was constructed inside the superpoint to capture the context information of the point cloud in the scene and use the attention mechanism module to highlight the key information.Finally,a SuperPoint Graph(SPG)model was constructed,and the features of hyperpoints and hyperedges were aggregated by Gated Recurrent Unit(GRU)to realize accurate segmentation among different land types of point cloud.On Semantic3D dataset,the semantic segmentation effect of A-Edge-SPG model and SPG-Net(SPG neural Network)model was compared and analyzed.Experimental results show that compared with the SPG model,A-Edge-SPG model improves the Overall segmentation Accuracy(OA),mean Intersection over Union(mIoU)and mean Average Accuracy(mAA)by 1.8,5.1 and 2.8 percentage points respectively,and significantly improves the segmentation accuracy of similar land types such as high vegetation and dwarf vegetation,improving the effect of distinguishing similar land types.