结合多尺度体素和高阶条件随机场的点云分类

针对利用经典高阶条件随机场模型进行点云分类时,由于海量节点和无向边导致的点云分类效率低的问题,提出一种结合多尺度体素和高阶条件随机场的点云分类方法.首先以多尺度体素代替海量离散点云作为无向图图模型节点,减少节点和无向边的数量;然后使用超体分割结果作为高阶团,并基于此设计了一种非监督分布性空间上下文作为高阶团特征向量,用于改善分类结果;最后结合构建的图模型和各阶特征向量,采用经典高阶条件随机场模型实现点云数据的自动分类.采用Oakland标准数据集作为实验数据,实验结果表明,该方法在有效地保证分类精度的前提下,高阶条件随机场点云分类模型的分类效率提高了5～10倍.

A Point Cloud Classification Method Based on Multi-scale Voxel and Higher Order Random Fields

Aiming at solving the problem of low efficiency of point cloud classification caused by massive nodes and undirected edges when using traditional high-order conditional random field model,a point cloud classification method based on multi-scale voxel and high order random fields is proposed.Firstly,multiscale voxel is utilized as a node of undirected graph to replace the mass of discrete point clouds and reduce the number of nodes and undirected edges.Then,the supervoxel segmentation result is used as a higher-order cluster based on which an unsupervised distributed spatial context is designed as higher-order cluster eigenvector to improve the classification result.Finally,combined with the constructed graph model and each order eigenvector,classical high-order conditional random field model is implemented for automatic point cloud data classification.The Oakland standard dataset is used as the experimental data.Experimental results show that the classification efficiency of the high-order conditional random field point cloud classification model is improved by 5 to 10 times under the premise of ensuring the classification accuracy.