利用局部熵和重复度检测特征点

Feature Point Detection Based on Local Entropy and Repeatability Rate

以三维散乱点在局部邻域内的熵变化为检测准则,利用局部熵突变发生在曲面形状变化剧烈区域的特性,描述采样点属于某个特征的可能性;同时引入重复度;以反映在不同大小的局部窗口下采样点被检测为特征点的频度,获取特征点集实验结果表明,该算法稳定性较好。

Local entropy of data points changing sharply over neighborhood is introduced as a detection criterion to classify points as a feature, where the local surface curvature changes greatly. Repeatability rate is introduced as well to reflect the frequency that a sample point is detected as a feature point during its verification at different sizes of local windows. Experiments show that such a multi-scale feature point detection approach can improve the reliability of the algorithm. Furthermore, non-uniformly sampled point cloud can be dealt with.