This manuscript is dedicated to the detection and caracterization of interest points for 3D meshes. First of all, we show the limitations of the curvature measure on sharp edges, the measure usually used for the analysis of meshes. Then, we present a generalization of the SUSAN operator for meshes, named SUSAN-3D. The saliency measure proposed quantify the local variation of the surface and classify directly the analysed vertices in five classes: salient, crest, flat, valley and cavity. The meshes under consideration are manifolds and can be closed or non-closed, regulars or irregulars, dense or not and noised or not. The accuracy of the SUSAN-3D operator is compared to two curvature operators: the Meyer's operator and the Stokely's operator. Two comparison methods of saliency and curvature measures are described and used on two types of objects: spheres and cubes. The spheres allow the study of the accuracy for differentiable surfaces and the cubes for two types of sharp edges: crests and corners. Through these studies, we show the benefits of our method that are a strong repeatability of the measure, high robustness to noise and capacity to analyse non dense meshes. Finally, we present a multi-scale scheme and automation of the determination of the analysis scales that allow SUSAN-3D to be a general and autonomous operator for the analysis and caracterization of meshes
