Interpolate 3D scattered data to gridded data and compute their derivatives
(i) Interpolate 3D scatterred data to gridded data
(ii) Reduce noise by applying curvature regularization 
(iii) Generate central difference operator 
(iv) Compute first order derivatives
% ****** 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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 regularizeNd. https://www.mathworks.com/matlabcentral/fileexchange/61436-regularizend
 Augmented Lagrangian Digital Volume Correlation (ALDVC). https://www.mathworks.com/matlabcentral/fileexchange/77019-augmented-lagrangian-digital-volume-correlation-aldvc
 Streamcolor. https://www.mathworks.com/matlabcentral/fileexchange/24049-streamcolor
Jin Yang (2020). Gridded interpolation and gradients of 3D scattered data (https://github.com/jyang526843/Scatter2Grid3D), GitHub. Retrieved .
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