基于误差熵的点云简化方法

点云简化很难完全保证精度、简化率和速度上都达到最优。针对不同的表面特征状况，提出一种自适应点云简化算法。利用经典的PCA方法来估计点的法向量，计算法向量与参考平面的夹角，针对表面特征的不同，采用法向量夹角的熵来确定表面的特征状况。针对不同的表面特征来设置不同的简化率，从而获得较适宜的简化效果。实验表明，该方法在简化精度、简化率和速度上能达到一种平衡。

The Simplification Method Based on Error Entropy

Currently, using the point cloud simplification, it is hard to achieve high precision, superior simplification rate and high speed. This paper proposes an adaptive point cloud simplification algorithm. Firstly, we use the PCA to estimate the normal of each point and to compute the angle between the normal vector and the reference plane. The characteristics of the surface can be determined by the local entropy of normal vector angles. The superior results of simplification can be derived according to the different simplification rate, counter to the characteristics of the surface. The results show that the proposed approach can reach a balance in the simplification precision, simplification rate, and simplification speed.