Mesh segmentation guided by seed points

Abstract

Segmenting 3D models into meaningful parts is a fundamental problem in computer graphics. In this paper, we present an algorithm which is guided by the mesh vertices for segmenting a mesh into meaningful sub-meshes. First, a candidate set of feature points is selected to highlight the most significant features of the model. After that, a diversity measure is calculated by using Hausdorff distance. The collection of seed points we defined is a subset of the candidate set. The collection of seed points consists of two parts, namely, the basic point and lucky points. By maximizing the diversity measure between the seed set and candidate set, an appropriate seed point is selected. Based on the collection of seed points, the segmentation process can be guided by these seeds. Because humans generally perceive desirable segmentations at concave regions, and the geodesic distance and curvature are well-known noise sensitive. Considering the above factors, in order to partition the target into meaningful parts, we define a distance function between each pair of mesh vertices. This function is formed by arc length, angular distance and curvature-related correction term. We have tested the method developed in this paper with 3D meshes from the Princeton segmentation benchmark. Moreover, our segmentation method also has been compared with other methods. The experimental results have demonstrated the effectiveness of the proposed segmentation method.

3D Mesh Segmentation Based on Energy Optimization

“…现有的主流分割方法 大致可以分为 3 类. 第 1 类是根据局部或全局几何 特征将模型表面的相似部分合并为同一区域, 如区 域增长法 [6][7][8] 和聚类分析法 [9][10][11] 等, 这类方法受不同 几何特征影响较大, 分割结果难以预测, 往往存在 过分割或欠分割现象; 其中过分割指原本属于一个 整体的目标被过量分割成多个部分, 欠分割指目标 未被完整分割. 第 2 类是基于最小值原则 [12] , 在模 型表面构造出符合最小值原则的分割线, 通过这些 分割线将模型分割为若干部分, 这类方法的分割结 果仍然受不同几何特征的影响.…”

“…Hu 等 [9] 根据网格面的几何特征, 对 2 个网格面之间的相似度进行编码, 通过相似度 对子空间进行聚类; Jiao 等 [10] 首先根据视觉理论中 的最小规则确定网格的凹区域, 然后考虑网格的 显著性和曲率信息, 根据网格显著性来确定初始 聚类中心. Jiao 等 [11] 提出一种基于顶点的聚类方 法, 首先获取候选特征点集, 然后利用 Hausdorff 距离定义分集度量, 通过迭代过滤候选特征点集 得到种子点的集合. 这类方法无需依赖于模型的 拓扑结构, 但由于往往需要计算并存储各顶点与 聚类中心的测地距离, 所以效率较低.…”