点云的边缘是将点云模型转换为高质量曲面或实体模型的重要中间特征。为了有效地提取点云边缘,针对现有点云边缘检测算法设置统一邻域值或邻域半径造成的边缘检测不准确的问题,提出一种基于局部边缘特征描述子的自适应边缘检测算法。首先定义法向量特征模型,引入法向量变化率,基于法向量变化率提出邻域自适应方法;然后结合点云局部区域的曲率密度,定义局部边缘特征描述子;最后根据特征描述子取值符合高斯分布的特点自动调整阈值,解决了对于不同的点云模型需要人工调参的问题。通过在多种不同的点云数据集上进行实验,证明了该算法可以在保持模型原始信息的前提下,准确地提取模型的边缘信息,具有可重复性和一定的鲁棒性。

Edges in a point cloud are important intermediate features for structuring point clouds and converting them into high-quality surfaces or solid models. To effectively extract the edge of the point cloud, an adaptive point cloud edge detection method based on local edge feature descriptor is proposed herein. The proposed method aims at addressing the problem of inaccurate edge detection caused by setting a unified neighborhood value or neighborhood radius in the existing point cloud edge detection algorithm. First, we provide the definition of a normal vector feature model, introduce the normal vector change rate, and propose a neighborhood adaptive method based on the normal vector change rate. Second, we combine the curvature density of the local area of the point cloud to define the local edge feature descriptor. Finally, we automatically adjust the threshold according to the characteristics of the value of the feature descriptor consistent with the Gaussian distribution, which solves the problem of manually adjusting the parameters for different point cloud models. Experiments on a variety of different point cloud datasets prove that the algorithm can accurately extract model edge information while maintaining the original information of the model. Furthermore, it exhibits repeatability and certain degree of robustness.