顾及结构特征的点云自适应精简算法

Adaptive point clouds reduction algorithm considering structural features

散乱点云的精简是点云进行曲面重构工程中重要的数据处理步骤之一。针对点云精简过程中的精简结果失真、点云数据空洞、结构特征易损失的问题,提出一种顾及结构特征的点云自适应精简方法。采用多个公开点云数据模型验证算法,以精简度、点间距标准偏差、模型表面积变化率、模型体积变化率定量评价算法的有效性。结果表明,该算法能够应用于含有平面、曲面和多特征的点云数据,精简后的点云模型特征突出、均匀无空洞、结构特征信息损失低,可以实现在点云数据精简的同时,保留原始结构特征信息。

The reduction of scattered point cloud is one of the important data processing steps in surface reconstruction engineering. In order to solve the problems such as distortion of reduction results,hole of point cloud data and easy loss of structural features,an adaptive point cloud reduction method considering structural features is proposed. The effectiveness of the algorithm is evaluated quantitatively by the simplification degree,standard deviation of point spacing,model surface area change rate and model volume change rate. The results show that the algorithm can be applied to the point cloud data with plane,surface and multi features. The simplified point cloud model has outstanding features,uniform without holes,and low loss of structural feature information in order to achieve the point cloud data reduction while retaining the original structure feature information.