激光与光电子学进展, 2019, 56 (9): 091002, 网络出版: 2019-07-05
基于k -means聚类的点云精简方法 下载: 507次
Point Cloud Simplification Method Based on k -Means Clustering
图像处理 点云精简 k均值聚类 曲面拟合 均方根曲率 压缩率 image processing point cloud simplification k-means clustering surface fitting root mean square curvature compression rate
摘要
提出了一种基于k 均值(k -means)聚类的点云精简方法。与包围盒法相比,在压缩率近似相同的条件下,k -means聚类方法能较好地保留细节特征,与原始数据的稠密稀疏分布更加一致,所建模型表面更光滑。
Abstract
A point cloud simplification method is proposed based on k-means clustering. Compared with the bounding box method with a similar compression rate, the k-means clustering method can preserve the details better, and the result is more consistent with the dense and sparse distribution of the original data. Moreover, the surface of the constructed model is smoother.
贺一波, 陈冉丽, 吴侃, 段志鑫. 基于