基于邻域点位置特征的点云数据精简

Point-Cloud Data Reduction Based on Neighborhood-Point Position Feature

海量点云数据给存储、传输、处理等带来极大困难。针对现有算法在特征保留与精简后重建模型表面积、体积、重建误差不能兼顾的问题,提出一种基于邻域点位置特征的点云精简算法。该算法根据权值计算投影面、搜寻矩阵大小以及精简比例对目标点云进行精简。将目标点云网格化处理;寻找投影面垂直方向(正、负两个方向),以目标点为中心,获取搜寻矩阵范围内的点;根据搜寻矩阵内点与目标点的位置关系确定其权值;根据所设的精简比例对原始点云进行精简。将所提算法与曲率采样法、均匀网格法和随机采样法进行比较,并从特征保留、表面积和体积变化率这3个方面进行评价。实验结果表明:所提算法的精简结果对特征区域效果优于均匀网格法和随机采样法,与曲率采样法一致;精简结果误差、重建模型的表面积差和体积差总体优于曲率采样法,与随机采样法基本一致,略差于均匀网格法。因此,所提算法既能较好地保留特征,同时又能使重建后的结果模型表面积和体积变化以及误差都较小,综合效果好。

Massive point-cloud data involve considerable difficulties in storage,transmission,and processing.To address the problem that existing algorithms cannot consider the surface area,volume,or reconstruction error of the reconstructed model after feature preservation and simplification,we propose a point-cloud simplification algorithm based on the location features of neighboring points.The algorithm simplifies the target point-cloud according to the weight calculation projection plane,search matrix size,and reduction ratio.To mesh the target point-cloud,we find the vertical direction of the projection plane(positive and negative directions),take the target point as the center,and obtain the points within the search matrix.The weight value is determined according to the position relationship between the point in the search matrix and the target point,and the original point-cloud is reduced according to the reduction ratio set.The proposed algorithm is compared with curvature sampling,uniform grid,and random sampling methods,and is evaluated in terms of feature retention,surface area,and rate of change of volume.Experimental results show that the reductions performed by the proposed algorithm are better than those provided by the uniform grid and the random sampling methods for feature regions,and are consistent with the curvature sampling method.The reduction causes the error,surface product difference,and volume difference of the reconstructed model to be generally superior to those of the curvature sampling method,consistent with the random sampling method,and slightly inferior to those of the uniform grid method.Therefore,the proposed algorithm not only preserves features,but also reduces the variation and error in the surface area and volume of the reconstructed model.