Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (17): 181-186.DOI: 10.3778/j.issn.1002-8331.1909-0305
MA Jinghui, PAN Wei, WANG Ru
Aiming at the problem that the performance of 3D point-cloud classification algorithm is affected by point-cloud sparsity and disorder, this paper proposes an improved algorithm based on PointNet which is proposed in 2018. Firstly, during the point-cloud preprocessing, redundant data are removed from dense point-clouds to reduce the complexity of subsequent work. And at the same time, triangle interpolation is used in the sparse point-cloud data to make the classification more precise. Secondly, it uses K-means algorithm to cluster the preprossed data and put them through the PointNet network in parallel. The distribution characteristics of point-cloud can be obtained by this way. Experiments are made on ModelNet10/40 and compared with some popular classification algorithms based on deep learning. The results show that the performance of this new algorithm is the best in the above algorithms while the training time is greatly reduced.
K-means clustering analysis,
3D point cloud classification,
MA Jinghui, PAN Wei, WANG Ru. 3D Point Cloud Classification Based on K-means Clustering[J]. Computer Engineering and Applications, 2020, 56(17): 181-186.
马京晖，潘巍，王茹. 基于K-means聚类的三维点云分类[J]. 计算机工程与应用, 2020, 56(17): 181-186.
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