结合上下文特征和图割算法的车载点云聚类方法

doi:10.3969/j.issn.0255-8297.2020.06.009

Abstract

Abstract: Reliable and accurate clustering of point cloud is the basis for subsequent high-precision analysis and interpretation of scene target. This paper presents a method for vehicle point cloud segment using supervoxel and graph-cut with contextual feature. Firstly, density-based spatial clustering of applications with noise (DBSCAN) is used to segment the point cloud data, and density-reachable supervoxels can be obtained. Secondly, spatial and attribute contextual features are introduced to describe the correlation between supervoxels and to define the weights of the edges of the graph model constructed by supervoxels. Finally, the optimal supervoxel clustering is obtained based on multi-label graph-cut optimization algorithm. Experimental results show that the proposed method has improved accuracy and performance on over segmentation in clustering.

Key words: density-based spatial clustering of applications with noise (DBSCAN), supervoxel, contextual feature, graph cut, point cloud clustering

CLC Number:

P208

LIU Yawen, ZHANG Ying. Vehicle Point Cloud Clustering Based on Contextual Feature and Graph Cut[J]. Journal of Applied Sciences, 2020, 38(6): 924-935.

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Vehicle Point Cloud Clustering Based on Contextual Feature and Graph Cut

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