create a 3D smoothing with csaps (or similar) (2024)

% load curve_1

% cc = curve_1; %or curve_2

load curve_2

cc = curve_2; %or curve_2

x = cc(:,1);

y = cc(:,2);

z = cc(:,3);

% unique & sort z ascending (for interpolation purposes)

[z,ia,ic] = unique(z);

x = x(ia);

y = y(ia);

% interpolation first

z_min = min(z);

z_max = max(z);

zi = linspace(z_min,z_max,100);

xi = interp1(z,x,zi,'spline');

yi = interp1(z,y,zi,'spline');

% smoothing (if needed)

[zzz,s,exitflag] = smoothn({xi,yi,zi},1);

xn = zzz{1};

yn = zzz{2};

zn = zzz{3};

plot3(x,y,z,'dr');

hold on

plot3(xi,yi,zi,'*g');

plot3(xn,yn,zn,'b');

hold off

load curve_1

cc = curve_1; %or curve_2

% load curve_2

% cc = curve_2; %or curve_2

x = cc(:,1);

y = cc(:,2);

z = cc(:,3);

% unique & sort z ascending (for interpolation purposes)

[z,ia,ic] = unique(z);

x = x(ia);

y = y(ia);

% interpolation first

z_min = min(z);

z_max = max(z);

zi = linspace(z_min,z_max,100)';

xi = interp1(z,x,zi,'spline');

yi = interp1(z,y,zi,'spline');

% polynomial fit

% % FEX : https://fr.mathworks.com/matlabcentral/fileexchange/34765-polyfitn?s_tid=ta_fx_results

order = 1;

p = polyfitn([xi,yi],zi,order);

pC = p.Coefficients; % get the polynomial coefficients

pTerms = p.ModelTerms;

% create the polynomial model (z = f(x,y))

zt = 0;

for k = 1:numel(pC)

zt = zt + pC(k)*(xi.^pTerms(k,1)).*(yi.^pTerms(k,2)); %

end

figure(1),

plot3(x,y,z,'r*',xi,yi,zt,'k','linewidth',2); %

xlabel('X');

ylabel('Y');

zlabel('Z');

legend('raw data','fitted curve');

axis tight square