See my comment on why you likely don't want to do this, but the general way of plotting in 3D is

x = 1:10;
y = 1:5;
[X Y] = meshgrid(x, y);
Z = X.^2 + 2 .* Y;      % in general, Z = f(X, Y)
plot3(X, Y, Z, '+')

Try this slightly augmented version — figure(1) hold on grid minor axis([0 2 0 2 0 2]) for i = 1:numel(DD) view(3) set(gcf, 'Color', 'White'); set(gca, 'Fontsize', 12); set(gca, 'ZDir','reverse') scatter3(DD{i}(:,2),DD{i}(:,3),DD{i}(:,4)) grid on axis([0 2 0 2 0 2]) drawnow pause(0.25) xlabel('x [mm]') ylabel('y [mm]') zlabel('z [mm]') end hold off It fixes the axis limits and adds drawnow and pause to create the animation. Experiment to get the desired results. .

I think you are looking for meshgrid. [y, x, z] = meshgrid(1:3, 1:3, 1:3); scatter3(x(:), y(:), z(:), [], amplitude(:), 'o', 'filled'); Notice that it is [y, x, z] and not [x, y, z], it is a quirk of the meshgrid function.

3D density plot demos Here's an assortment of 3D density visualizations though there are many other ways to visualize 3D density. The script was written on the fly and each demo can be cleaned up and optimized. Consider it a sketch, a scratch pad, a Jackson Pollock painting, or a wall in an italian restaurant after a long day of "see what sticks" tests. All plots show the same data displayed using different visualizations. Some plots require recent release of matlab (e.g. bubblechart3 - r2020b). Control the number of bins with the nBins variable. See inline comment for details. %% Create 3D scatter data n = 2000; xyz = zeros(n,3); xyz(:,1) = randn(n,1).*2+8; xyz(:,2) = randn(n,1).*5-2; xyz(:,3) = randn(n,1).*3+4; ri = randperm(n,round(n*.1)); xyz(ri,:) = xyz(ri,:).*[.8,.4,.5]+[-8,12,6]; %% Compute density % Put points into 3D bins; xyzBinNum is an nx3 matrix containing % the bin ID for n values in xyz for the [x,y,z] axes. nBins = 8; % number of bins xbins = linspace(min(xyz(:,1)),max(xyz(:,1))*1,nBins+1); ybins = linspace(min(xyz(:,2)),max(xyz(:,2))*1,nBins+1); zbins = linspace(min(xyz(:,3)),max(xyz(:,3))*1,nBins+1); xyzBinNum = [... discretize(xyz(:,1),xbins), ... discretize(xyz(:,2),ybins), ... discretize(xyz(:,3),zbins), ... ]; % bin3D is a mx3 matrix of m unique 3D bins that appear % in xyzBinNum, sorted. binNum is a nx1 vector of bin % numbers identifying the bin for each xyz point. For example, % b=xyz(j,:) belongs to bins3D(b,:). [bins3D, ~, binNum] = unique(xyzBinNum, 'rows'); % density is a mx1 vector of integers showing the number of % xyz points in each of the bins3D. To see the number of points % in bins3D(k,:), density(k). density = histcounts(binNum,[1:size(bins3D,1),inf])'; % Compute bin centers xbinCnt = xbins(2:end)-diff(xbins)/2; ybinCnt = ybins(2:end)-diff(ybins)/2; zbinCnt = zbins(2:end)-diff(zbins)/2; %% Plot raw data fig = figure(); tiledlayout('flow') nexttile() plot3(... xyz(:,1), ... xyz(:,2), ... xyz(:,3), '.') title('Raw data') grid on box on %% Plot bubblechart3 nexttile() bubblechart3(... xbinCnt(bins3D(:,1)), ... ybinCnt(bins3D(:,2)), ... zbinCnt(bins3D(:,3)), ... density, ... density, ... 'MarkerFaceAlpha', .3, ... 'MarkerEdgeAlpha', .3) title('bubblechart3') %% Plot scatter3 nexttile() scatter3(... xbinCnt(bins3D(:,1)), ... ybinCnt(bins3D(:,2)), ... zbinCnt(bins3D(:,3)), ... density*5+5, ... density, 'filled', ... 'MarkerFaceAlpha',.6) title('scatter3') box on %% Plot marginal histograms % Histograms are normalized and are not related to % the axis ticks though their relative height within % the axes shows the population density (ie, 30% of % the axis hight means that the bin contains 30% of % the data. nexttile() plot3(... xyz(:,1), ... xyz(:,2), ... xyz(:,3), '.') grid on box on hold on xcount = histcounts(xyz(:,1),xbins)/size(xyz,1)*range(zlim())+min(zlim()); ycount = histcounts(xyz(:,2),ybins)/size(xyz,1)*range(zlim())+min(zlim()); % x density plotted on xz plane patch(repelem(xbins(:),2,1), ... repmat(max(ybins),numel(xbins)*2,1), ... [min(zbins);repelem(xcount(:),2,1);min(zbins)], ... 'r','FaceAlpha',.25) % y density plotted on yz plane patch(repmat(max(xbins),numel(xbins)*2,1), ... repelem(ybins(:),2,1), ... [min(zbins);repelem(ycount(:),2,1);min(zbins)], ... 'r','FaceAlpha',.25) title('Marginal histograms') %% Plot marginal surface plots % All 3 surface planes use the same color limit. nexttile() scatter3(... xyz(:,1), ... xyz(:,2), ... xyz(:,3), ... 10, 'w', 'filled', ... 'MarkerFaceAlpha', .5) hold on grid off xlim([min(xbins), max(xbins)]) ylim([min(ybins), max(ybins)]) zlim([min(zbins), max(zbins)]) % xz plane density [xm,zm] = ndgrid(xbins, zbins); xzCount = histcounts2(xyz(:,1),xyz(:,3), xbins,zbins); surf(xm,max(ylim())*ones(size(xm)),zm,xzCount,'FaceAlpha',.8) % yz plane density [ym,zm] = ndgrid(ybins, zbins); yzCount = histcounts2(xyz(:,2),xyz(:,3), ybins,zbins); surf(max(xlim())*ones(size(xm)),ym,zm,yzCount,'FaceAlpha',.8) % xy plane density [xm,ym] = ndgrid(xbins, ybins); xyCount = histcounts2(xyz(:,1),xyz(:,2), xbins,ybins); surf(xm,ym,min(zlim())*ones(size(xm)),xyCount,'FaceAlpha',.8) set(gca,'LineWidth',3) box on maxCount = max([xyCount(:);xyCount(:);xzCount(:)]); set(gca,'colormap',hot(256),'CLim',[0,maxCount]) cb = colorbar(); ylabel(cb,'Number of dots') title('Density Planes') %% Equate all axis limits fig.UserData.hlink = linkprop(findobj(fig,'type','axes'),{'xlim','ylim','zlim'});

A simple linear regression is straightforward: x = [14.9 1.7 0.0 10.9 0.0]; y = [11.3 9.1 23.7 12.8 2.9]; z = [5.32787E-17 2.93234E-16 2.09997E-16 5.45E-17 4.55E-16]; B = [x(:) y(:) ones(size(x(:)))] \ z(:); xv = linspace(min(x), max(x), 10)'; yv = linspace(min(y), max(y), 10)'; [X,Y] = meshgrid(xv, yv); Z = reshape([X(:), Y(:), ones(size(X(:)))] * B, numel(xv), []); scatter3(x,y,z, 'filled') hold on mesh(X, Y, Z, 'FaceAlpha', 0.5) hold off view(-120, 35) title(sprintf('Z = %+.3E\\cdotX %+.3E\\cdotY %+.3E', B)) producing:

[X,Y,Z] = ndgrid(1:size(cube,1), 1:size(cube,2), 1:size(cube,3)); pointsize = 30; scatter3(X(:), Y(:), Z(:), pointsize, cube(:));