基于K-means聚类的三维点云分类

3D Point Cloud Classification Based on K-means Clustering

针对三维点云分类算法受到点云稀疏性和无序性影响的问题,提出一种改进算法。在点云预处理阶段,对密集的点云进行冗余数据去除,以减少后续计算量;对于稀疏的点云数据则进行三角形插值计算,以使分类更精确。加入K-means聚类分析算法,之后并行通过PointNet网络进行特征提取,该方法可体现点云空间中的点云分布特性。分别在ModelNet10/40上进行三维点云分类实验,并对比不同K值对分类结果的影响。实验结果表明,当K=5时分类准确率最高,其在ModelNet10/40上的准确率分别是94.2%和92.6%。提出的算法性能高于其他对比算法,同时训练时间大大减少。

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.