摘要
针对大规模离散点云搜索k邻域速度慢的问题,提出了一种新的搜索k邻域算法,该算法根据不同点附近点云密度给出一个合适的点的k邻域动态球半径,且动态球半径是随着所求点周围点云的密度而自适应的。从离散点云分块大小和采样密度方面对算法的可行性和效率进行了实验验证,结果显示,运用该算法求取每个点的k邻域所用的搜索时间更短,效率更高。
In order to solve the problem of large scale discrete point cloud searching for the k neighborhood slowly,it proposes a new k algorithm based on the former. In this paper the radius of the discrete point cloud of k neighborhood is adaptive with the density of the point cloud. According to the point cloud density near the point of the different point,it finds a suitable radius,analyzes the search efficiency of k neighborhood of discrete points from the aspects of the discrete point cloud block size and the sampling density,verifies the feasibility and efficiency of the algorithm.
出处
《机械设计与制造工程》
2016年第6期83-86,共4页
Machine Design and Manufacturing Engineering
关键词
k邻域
自适应
离散
采样密度
k neighborhood
adaptive
discrete
sampling density
