一种特征感知的三维点云简化算法

Feature-Aware Three-Dimensional Point Cloud Simplification Algorithm

提出一种特征感知的三维点云简化方法。通过构造八叉树搜索每个点的k近邻点,并计算每个点的法向量,以此检测并保留边缘点;使用期望最大化算法对点云进行聚类,并确定高曲率的点;使用边缘感知的有向Hausdorff距离方法进行点云精简,合并前述点云并删除重复点,实现模型简化。该方法适用于不同曲率变化的模型,并且能够在保留尖锐特征的同时显示模型整体轮廓。实验结果表明,该方法不仅能够保留原始模型的几何特征和轮廓外貌,而且有效地避免了简化过程中的孔洞现象,几何简化误差较低。

In this paper,we propose a simplified method of feature-aware for a three-dimensional point cloud.First,the k-nearest neighbor points of each point are searched by constructing an octree,and the normal vector of each point is calculated to detect and preserve the edge points.Then,the expectation maximization algorithm is utilized to cluster the point clouds and determine the points with high curvature.Finally,these point clouds are simplified by a method which utilizes the edge-aware directed Hausdorff distance,the above point clouds are merged,the duplicate points are deleted,and thus,the model is simplified.The proposed method is suitable for the models with different curvature changes,and it can display the overall contour of the model while retaining the sharp features.The experimental results show that the proposed method not only preserves the geometric features and contour appearance of the original model,but also effectively avoids the hole phenomenon in the simplification process.The geometric simplification error of the method is considerably low.