中文摘要：

我们为由把正常张肌投票与邻居支持相结合在三角形的网孔上检测特征建议一个柔韧的方法。我们的方法包含二个阶段：展示察觉和特征精炼。首先，正常张肌投票的方法被修改检测起始的特征，它可以包括一些假特征。在特征精炼阶段，然后，源于邻居支持的想法的一项新奇突出的措施被开发。得益于综合可靠突出的措施特征，假特征能有效地从开始检测的特征被区别并且搬迁。基于微分几何性质比作以前的方法，我们的方法的主要优点是它能检测锋利、弱的特征。数字实验证明我们的算法是柔韧的，有效，并且能生产更精确的结果。我们也讨论怎么检测了特征被合并到应用程序，例如保存特征的网孔降噪和由集成特征信息的充满洞、现在的视觉上呼吁的结果。

英文摘要：

We propose a robust method for detecting features on triangular meshes by combining normal tensor voting with neighbor supporting. Our method contains two stages： feature detection and feature refinement. Firsts the normal tensor voting method is modified to detect the initial features, which may include some pseudo features. Then, at the feature refinement stage, a novel salient measure deriving from the idea of neighbor supporting is developed. Benefiting from the integrated reliable salient measure feature, pseudo features can be effectively discriminated from the initially detected features and removed. Compared to previous methods based on the differential geometric property, the main advantage of our method is that it can detect both sharp and weak features. Numerical experiments show that our algorithm is robust, effective, and can produce more accurate results. We also discuss how detected features are incorporated into applications, such as feature-preserving mesh denoising and hole-filling, and present visually appealing results by integrating feature information.