提出一种新的3维模型的特征点检测算法。该算法可以作为其他许多3维模型处理技术的预处理操作(如模型简化、模型匹配、视点选择等)。与其他3维模型特征点检测算法相比，该算法具有两个特点： 1)引入一种新的显著性度量方法——“局部高度”，而不是传统的曲率。认为3维模型表面某点的视觉重要性(即显著性)是由它所在位置的凸起程度来刻画，而不是该点所在位置的弯曲程度所决定，因此，提出局部高度这种新的显著性度量方式。 2)基于局部高度，引入Mean Shift算法这种非参数化的概率密度估计方法来对3维模型表面的局部高度分布进行聚类分析，然后计算出3维模型的特征点。实验结果表明，该算法能够很好地捕捉视觉上显著的3维模型特征点，且在不同分辨率下均有稳定的表现。

In this paper a new feature point detection method for 3D meshes is proposed. This method serves as an important preprocessing step for a number of 3D applications including mesh simplification, 3D shape matching, and viewpoint selection. Compared with similar algorithms recently proposed, the proposed method has two advantages: 1) our feature point detection algorithm is based on our new perceptual saliency measure, using the local height, rather than being based on traditional curvature. We assume that the perceptual importance of a given point on a 3D model can be described by the protrusiveness of that point, but not the bend degree. Therefore, we proposed the local height as a new measure for evaluating the perceptual importance of a point. 2) We use Mean Shift, a powerful non-parametric estimator of the density gradient, to analyze the distribution of local heights on a mesh, and to detect feature points on this meshExperimental results showed that our proposed method is able to capture perceptually salient feature points on a 3D method,and the algorithm is stable at different levels of details.