提出一种通过种子边缘点提取闭合点云边缘的算法,通过搜索视角控制边缘点云搜索方向,可提取尖锐边缘与相交边缘。算法通过模拟点与点之间拉力的聚集程度来判断一个点是否具有边缘点的特征。首先构建kd-tree以便快速查找点云数据。其次定义点云的邻域半径,通过计算种子点邻域内点的边缘系数来选择新的种子边缘点,并通过搜索视角来控制点云搜索的方向,最终提取闭合的点云边缘。选择不同的种子点与搜索视角可提取不同的边缘。通过实验分析邻域半径和搜索视角对算法边缘提取效果的影响,并采用合适的半径与搜索视角,对尖锐边缘与相交边缘进行点云提取,获得了准确的点云边缘,从而证明了该算法的有效性。

A new algorithm is proposed to extract the edge of the closed point cloud through the edge seed point. Using the algorithm can extract the sharp edge and intersecting edge by setting an appropriate search angle. The algorithm determines whether a point has the characteristic of edge point by simulating the aggregation degree of pull between points. First, build the kd-tree to find each point quickly. Second, define the neighbor radius of the point cloud, and select the new edge points by calculating the edge coefficient of the seed point located in the neighbor radius. Use the search angle of the seed point to control the search direction. Finally, extract the closed point cloud's edge. Choosing different seed points and search angles can extract different edges. The effects of the neighbor radius and the search angle were analyzed by the experiment. The appropriate neighbor radius and search angle were used to extract the sharp edge and the intersecting edge, and the accurate edge of the point cloud was obtained, which proves the effectiveness of the algorithm.