Finely captured meshes often contain details like sharp edges, corners and shallow features. In order to improve the quality of these meshes, we present a robust and efficient high-resolution details-preserving mesh denoising algorithm. Our method consists of three stages. For each triangular face and its face neighborhood, we improve a robust density-based clustering method and apply it to the face neighborhood to extract a subset of neighbors which belong to the same cluster as the central face. And then, we filter the central face normal iteratively within this subset to remove noise. Because the faces within the extracted subset are not distributed across high-resolution details, our normal filtering can preserve such details as much as possible. Finally, we update the vertex positions to be consistent with the filtered face normals using a least-squares formulation. Experiments on various types of meshes indicate that our method has advantages over previous surface denoising methods. Finely captured meshes often contain details like sharp edges, corners and shallow features. In order to improve the quality of these meshes, we present a robust and efficient high-resolution details-preserving mesh denoising algorithm. triangular face and its face neighborhood, we improve a robust density-based clustering method and apply it to the face neighborhood to extract a subset of neighbors which belong to the same cluster as the central face. And then, we filter the central face normal iteratively Because this faces to remove noise. Because the faces within the extracted subset are not distributed across high-resolution details, our normal filtering can preserve such details as much as possible. Finally, we update the vertex positions to be consistent with the filtered face normals using a least-squares formulation. Experiments on various types of meshes indicate that our method has advantages over previous surface denoising metho ds.