基于局部曲面拟合的散乱点云简化方法

A Simplification Method for Cloud Points Based on Local Surface Fitting

随着数据获取手段的进步,散乱点云数据在三维重建中获得越来越广泛的应用,然而庞大的数据量往往影响重建的效率。现有简化算法中采用的曲率计算方法精度不高,导致模型特征模糊。本文在分析曲面特征的基础上给出了一种曲面特征的定量描述方法。该方法采用局部曲面拟合得到曲面在一点处的近似曲面,然后用法曲率在360度范围内的平均值代替平均曲率来描述曲面在一点处的特征。简化时采用K-D树剖分点云数据,根据子节点所包含的采样点数、空间区域大小和曲面特征大小控制简化过程。实验结果表明,该方法能够更好地保持曲面的几何特征,从而证明了算法的有效性。

With the improvement of the technology of data acquirement,the cloud-point data is used more and more widely in 3D reconstruction.The huge data size becomes the bottleneck of reconstruction efficiency.The feature of models is blurred because of the calculation accuracy of the curvature used in the existing simplification methods.A quantitative definition of the surface feature is proposed based on qualitative analysis.The approximate surface near a sampled point is obtained by the local surface fitting method.Then the feature of the surface near the sampled point is described by the average of the normal curvature in 360 degree instead of the average curvature.A K-D tree partitioning method is adopted to segment the cloud points according to the surface feature,the size of space area and the size of sampling nodes.Experiments show that this method preserves the geometry feature of the surface better.This result demonstrates the efficiency of the method.