Kernel smoothing of mesh

Gallery
6

Here the code

        /// <summary>
        /// Voir https://en.wikipedia.org/wiki/Kernel_(statistics)
        /// </summary>
        /// <param name="arg_meshECEF"></param>
        /// <param name="arg_tab_values">Values to be smoothed at mesh vertex</param>
        /// <param name="arg_KernelSize">Size of kernel, same unit as mesh</param>
        /// <param name="arg_logInterpolation">if true, arg_tab_values will be smoothed using thes log values</param>
        /// <returns>array of smoothed datz</returns>
        public static double[] FilterGaussianKDE(Mesh arg_meshECEF, double[] arg_tab_values, double arg_KernelSize, bool arg_logInterpolation)
        {
            if (arg_meshECEF.Vertices.Count != arg_tab_values.Length)
            {
                throw new Exception("Filtre KDE : Il n'y a pas le même nombre de points entre le maillage et les données sous forme tabulaire ");
            }
            double[] filteredData = new double[arg_meshECEF.Vertices.Count];
            double n = (double)arg_meshECEF.Vertices.Count;
            int i = 0;
            foreach (Point3d ptECEF in arg_meshECEF.Vertices)
            {
                int j = 0;
                double sumOfKernel = 0.0;
                foreach (Point3d pt2ECEF in arg_meshECEF.Vertices)
                {
                    double distSquared = pt2ECEF.DistanceToSquared(ptECEF);
                    double kernel = 0;

                    if (arg_KernelSize > 1e-6)
                    {
                        kernel = Math.Exp(-distSquared / (2 * arg_KernelSize * arg_KernelSize));//Kernel de type Gaussien
                    }
                    else
                    {
                        if (distSquared < 1e-6)
                        {
                            kernel = 1;
                        }
                        else
                        {
                            kernel = 0.0;
                        }
                    }
                    if (arg_logInterpolation)
                    {
                        filteredData[i] = filteredData[i] + Math.Log10(arg_tab_values[j]) * kernel;
                    }
                    else
                    {
                        filteredData[i] = filteredData[i] + arg_tab_values[j] * kernel;
                    }
                    sumOfKernel = sumOfKernel + kernel;
                    j++;
                }
                if (arg_logInterpolation)
                {
                    filteredData[i] = Math.Pow(10, filteredData[i] / sumOfKernel);
                }
                else
                {
                    filteredData[i] = filteredData[i] / sumOfKernel;
                }
                i++;
            }
            return filteredData;
        }
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