Curve Skeleton Extraction From 3D Point Clouds Through Hybrid Feature Point Shifting and Clustering
Curve Skeleton Extraction From 3D Point Clouds Through Hybrid Feature Point Shifting and Clustering
复制标题
通过混合特征点移动和聚类从 3D 点云中提取曲线骨架
DOI：
10.1111/cgf.13906
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发表时间：
2020
影响因子：
2.5
通讯作者：
Shen Yi
中科院分区：
文献类型：
--
作者：
Hu Hailong;Li Zhong;Jin Xiaogang;Deng Zhigang;Chen Minhong;Shen Yi
曲线骨架是一种重要的形状描述符，在计算机图形学、可视化和机器智能等领域有着广泛的应用。提出了一种基于点云模型截面形心集的曲线骨架表达方法，并提出了相应的提取方法。我们首先提供一个替代的距离场的三维点云模型，然后联合收割机它与曲率捕捉混合特征点。通过引入相关的面和点，我们将这些混合特征点沿着骨架引导的法线方向移动，以接近局部质心，通过基于张量的谱聚类简化它们，最后将它们连接起来，形成一个主连通曲线骨架。此外，我们通过修剪，修剪和平滑细化的主要骨架。我们将我们的结果与几种最先进的算法进行了比较，包括旋转对称轴（ROSA）和L1-medial方法，以评估我们方法的有效性和准确性。
Curve skeleton is an important shape descriptor with many potential applications in computer graphics, visualization and machine intelligence. We present a curve skeleton expression based on the set of the cross‐section centroids from a point cloud model and propose a corresponding extraction approach. We first provide the substitution of a distance field for a 3D point cloud model, and then combine it with curvatures to capture hybrid feature points. By introducing relevant facets and points, we shift these hybrid feature points along the skeleton‐guided normal directions to approach local centroids, simplify them through a tensor‐based spectral clustering and finally connect them to form a primary connected curve skeleton. Furthermore, we refine the primary skeleton through pruning, trimming and smoothing. We compared our results with several state‐of‐the‐art algorithms including the rotational symmetry axis (ROSA) andL1‐medial methods for incomplete point cloud data to evaluate the effectiveness and accuracy of our method.