于法向量夹角信息熵的点云简化算法

Point Cloud Simplification Based on the Information Entropy of Normal Vector Angle

针对点云简化很难完全保证精度和速度上达到最优的问题,提出了基于法向量夹角信息熵的点云简化算法。利用经典的主成分分析方法来估计点的法向量,计算法向量与参考平面的夹角,利用最邻近点搜索算法,确定每个点的K个最邻近点,并根据信息熵的定义,提出法向量夹角局部熵模型,局部熵的大小直接反映了表面的特征状况;针对不同区域局部熵大小,进行逐步的点云简化,从而可以保留凸变区域较多的点,精简较多平面区域的点,实现点云的非均匀简化。实验结果表明,该方法在简化精度和速度上都能达到较优。

A point cloud simplification based on the information entropy of normal vector angle is proposed, in view of the difficulty to ensure the optimal of precision and speed of simplification. The principal component analysis is used to estimate the normal of each point and the angle between normal vector and reference plane is computed. The K-nearest neighbor search algorithm is used to determine K-nearest neighbor points, and the local entropy of normal vector angle is proposed according to information entropy. The local entropy represents the features of surface. The point cloud is gradually simplified according to the different local entropy, the more points of convex region are retained and more points of plane are simplified, the non-uniform simplification is realized.The experimental results show that the proposed method can achieve a balance of precision and speed of simplification.