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.