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

被引：0
|
作者：
Shao L. [1 ,2 ]
Dong G. [1 ]
Yu Y. [1 ,3 ,4 ]
Zhang A. [1 ]
Yao Q. [1 ]
机构：
[1] Institute of Surveying and Mapping, Information Engineering University, Zhengzhou
[2] 75838 PLA Troops, Guangzhou
[3] Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing
[4] State Key Laboratory of Geo-Information Engineering, Xi'an
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 03期
关键词：
Conditional random field; Multi-scale voxel; Point cloud classification; Spatial contextual; Supervoxel segmentation;
D O I：
10.3724/SP.J.1089.2019.17116
中图分类号：
学科分类号：
摘要：
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. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码：385 / 392
页数：7
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