Mesh data is widely used in engineering for instance for simulations, CAD engineering and visualizations. The accuracy and quality of the meshes influence the reliability and validity of these processes. Besides manual modelling, scanning is becoming increasingly more common due to the increase in devices that have scanning capabilities. Unwanted noise is often present in scanned models. The process of mesh denoising is removing the unwanted noise whilst keeping the features of the mesh. These features are often anisotropic, e.g. sharp edges and corners. The DeltaConv convolution is an anisotropic convolution, Wiersma et al. [24] show the advantage of using the anisotropic DeltaConv convolution for anisotropic tasks over other isotropic convolutions. In this thesis it is investigated if state-of-the-art mesh denoising can benefit from using the DeltaConv convolution. This is done by integrating the DeltaConv convolution in the Dual-DMP [6] algorithm, and tuning this network. In this thesis we found that state-of-the-art mesh denoising can benefit from using the DeltaConv convolution. Due to the expressiveness of the DeltaConv convolution, objects with sharp features are denoised better than the state-of-the-art algorithms and on smooth meshes, DeltaConv works comparable to state-of-the-art algorithms.