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提出了一种快速提取散乱点云数据特征点方法,首先求出空间一点邻域内的曲面片模型,在此基础上利用梯度法搜索曲面上的高斯曲率极值点。然后再以该点作为搜索曲率极值点的初始点,根据判定准则搜索该点附近的曲率极值点。曲率极值点的搜索方法是边拟合局部曲面边搜索高斯曲率极值点,在搜索曲率极值点时,只需计算高斯曲率极值点附近点的曲率值。避免了传统算法中由于需要求出所有测量点的曲率值,然后进行比较求得曲率极值点而耗时间的缺点,从而提高了搜索效率。
In this paper, a method of extracting feature points of scattered point cloud data is proposed. Firstly, a patch model of a point in space is obtained. Based on the method, a gradient method is used to search for the extreme point of Gaussian curvature. Then the point is used as the initial point of searching for the extreme point of curvature, and the extreme point of curvature near the point is searched according to the criterion. The method of searching for the extreme points of curvature is to search for the extreme points of Gaussian curvature while fitting the local curved edges. When searching for the extreme points of curvature, only the curvature of the points near the extreme points of Gaussian curvature should be calculated. The traditional algorithm avoids the need to calculate the curvature of all the measurement points, and then compared to obtain the curvature of the extreme points and the time-consuming shortcomings, thereby enhancing the search efficiency.