基于体积积分不变量的散乱点云数据特征点提取

Extracting Feature Points for Scattered Points Based on Volume Integral Invariant

针对积分不变量不依赖网格拓扑结构的特性及体积积分不变量与平均曲率的内在联系,提出了一种基于平均曲率及体积积分不变量的散乱点云特征点提取方法。该算法采用4DShepard曲面估算点云曲率获得体积积分不变量,并基于体积积分不变量通过K-Means分类方法提取点云特征点。该算法只与点的个数和位置有关,实验结果表明,该算法不仅可以较快地提取特征点,而且表达点云边界特征点比较精确。

Based on the property of the integral invariant irrelevant to the meshes＇topology and the inner relation- ship between the volume integral invariant and the mean-curvature, a method Was proposed to extract the feature points from scattered point sets based on the mean-curvature and the volume integral invariant. The algorithm in the present method used the global 4D Shepard surface to estimate the curvature of each point in the point cloud and it was only related to the numbers and the operations of the points cloud. Then the volume integral invariant was cal- culated by the given formula, and the feature points were extracted through the K-Means clustering algorithm. The experiments show that this algorithm has higher computing efficiency, and can express the boundary feature of the clouds more precisely.