Part-based representation plays an important role in many shape related applications, including segmentation, recognition, editing and animation. An issue of particular interest in recent research is decomposing shapes into near-convex parts. However, it is usually challenging for existing methods to handle such heterogeneous real world shapes, especially when they possess long curved branches such as a lizard with a long curved tail. In this study, we propose a novel shape signature named visibility range, and a concavity measure based on this signature to describe the long curved branches. The visibility range reaches low values for points in concave regions and high values in convex regions, acting as the electrical charge distribution on the shape. Using these techniques, we present a coarse-to-fine approximate convex shape decomposition method, which separates the salient parts from the shape first and then refines the decomposition of the remaining main body of the shape by a visibility graph cut process. Qualitative and quantitative experiments have been conducted on shapes with various kinds of near-convex parts, demonstrating that our method captures the long curved branches as contiguous segments and outperforms the state-of-the-art methods that are based on other concave–convex features.

基于零件的表示在许多与形状相关的应用中扮演着重要的角色，包括分割、识别、编辑和动画。最近研究中特别感兴趣的一个问题是将形状分解成接近凸面的部分。然而，现有方法通常难以处理这种异质的现实世界形状，尤其是当它们具有长弯曲的分支时，例如具有长弯曲尾巴的蜥蜴。在这项研究中，我们提出了一种名为可见度范围的新形状特征，以及基于该特征的凹度度量来描述长弯曲分支。可见性范围在凹区中的点达到较低值，在凸区中达到高值，作为形状上的电荷分布。使用这些技术，我们提出了一种从粗到细的近似凸形分解方法，该方法首先将显着部分从形状中分离出来，然后通过可见性图切割过程细化形状剩余主体的分解。对具有各种近凸部分的形状进行了定性和定量实验，表明我们的方法将长弯曲分支捕获为连续的部分，并且优于基于其他凹凸特征的最新方法.