Gridded interpolation and gradients of 3D scattered data

Interpolate 3D scattered data to gridded data and compute their gradients

Interpolate 3D scattered data to gridded data and compute their derivatives
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(i) Interpolate 3D scatterred data to gridded data
(ii) Reduce noise by applying curvature regularization [1]
(iii) Generate central difference operator [2]
(iv) Compute first order derivatives

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% ****** ATTENTION ******
% The "x,y,z" or "1-,2-,3-" coordinates in this exchange file correspond to
% the 1st, 2nd and 3rd indices of Matlab workspace variable. For example,
% p_meas(:,1) and p_meas(:,2) are the x- & y-coordinates of scattered points.
%
% This is a little different from some MATLAB image processing functions.
% For example, if a 3D image has size M*N*L, in this code, we always have
% the image size_x=M, size_y=N, size_z=L. If you use some Matlab computer
% vision/image post-processing function, for example, 'imagesc3D', or
% 'imagesc', or 'imshow', or 'surf', it will reads size_x=N, size_y=M, size_z=L.
%
% Please pay attention to this.

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References
[1] regularizeNd. https://www.mathworks.com/matlabcentral/fileexchange/61436-regularizend
[2] Augmented Lagrangian Digital Volume Correlation (ALDVC). https://www.mathworks.com/matlabcentral/fileexchange/77019-augmented-lagrangian-digital-volume-correlation-aldvc
[3] Streamcolor. https://www.mathworks.com/matlabcentral/fileexchange/24049-streamcolor