基于高斯曲率极值点的散乱点云数据特征点提取

Extracting Feature Points for Scattered Points Based on Gauss Curvature Extreme Point

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。

A fast method was proposed to extract the feature points from scattered point sets. A local surface was constructed from a spatial point and its nearest neighbors, from which the maximal point of Gaussian curvature was computed by means of gradient searching. Taking this point as start point, the maximal curvature point was searched near the area of this point. An advantage of the scheme is the local surface was fitted, at the same time, the maximal point of Ganssian curvature was computed. When the maximal curvature point was calculated, only the area of maximal Gaussian curvature was searched, It could avoid the drawback of the computing the curvature for the whole points and the time cost of comparing the maximal curvatue point. So the new scheme has higher searching efficiency.