基于图像的重建点云离群点检测算法

Image Based Outlier Detection Algorithm for Point Cloud Reconstruction

基于图像重建出的三维点云模型通常会包含许多离群点,这些离群点可能孤立存在或密集聚集在一起形成点簇,也可能分布在模型周围甚至附着在模型表面。通过一种检测方法很难有效滤除多种分布状态的离群点,因此,提出了综合的离群点监测算法。首先通过空间距离剔除与模型主体较远的离群点,并通过构建空间拓扑关系加快离群点搜索速度;然后利用边界匹配法,将较小点簇分别与最大点簇进行对比,滤除模型周围离群点簇;最后采用改进的K-means算法,根据RGB颜色值特征对点云数据进行聚簇分类,结合已识别的离群点,检测和滤除附着在模型表面的离群点。仿真实验结果表明,此方法能够有效滤除点云模型中多种分布状态的离群点。

A reconstructed 3D point cloud model based on image usually includes many outliers.These outliers may exist in isolation or dense aggregation forming point clusters,and they may also be distributed around the model or even on the mode surface.It is difficult to filter out outliers of various distribution states through one detection method.Therefore,a comprehensive outlier detection algorithm is proposed.Firstly,the outliers far from the main body of the model are eliminated by space distance,and the search speed of outliers is accelerated by constructing the spatial topological relationship.Then,the boundary matching method is used to filter out the outliers around the model by comparing the smaller clusters with the largest ones respectively.Finally,an improved K-means algorithm is used to cluster and classify the point cloud data according to RGB color value features,and the outliers adhering to the model surface are detected and filtered by combining the identified outliers.Simulation results show that the proposed algorithm can effectively filter out the outliers with multiple distribution states in the reconstructed point cloud.