MATLAB: GriddedInterpolant for 3D matrix using grid vectors; not enough sample points

gridgriddedinterpolantMATLAB

I'm trying to interpolate a large 3D (time-series) double-floating-point variable in one direction efficiently by grid interpolation. Basically, my original matrix is structured as follows (block's colors are colorcoded by magnitude for illustrative purposes):
When I ran the below function on my dataset, it returned an error saying that "interpolation requires at least two sample points in each dimension" -even though my input matrix and grid vectors match (and my matrix is clearly multidimensional)… is there anything I'm doing that's obviously wrong?
function feature = myFunction(axial, lateral, t, d1, other variables)% "axial", "lateral", and "t" are vectors of different lengths% "d1" is a 3-D vector (axial x lateral x t)% "itpfctr" (interpolation factor) is derived somewhere along the lines...dt=(t(2)-t(1))*itpfctr;t_interp=(t(1):dt:t(end)); % upsample time vector; "dt" is named for later use        x=axial(:)';  y=lateral(:)';  z=t(:)'; % do these even need to be row vectors?ZZ=t_interp(:)';gv={x,y,z}; % the grid vector in questionF=griddedInterpolant(gv,d1,'cubic');d1_interp=F({x,y,ZZ});%... other calculations using d1_interp that output 2-D matrix "feature"

gv = {1:4, 1:5 , 1:6}gv =  1×3 cell array    {1×4 double}    {1×5 double}    {1×6 double}>> d1 = rand(4,5,6);>> F=griddedInterpolant(gv,d1,'cubic');>> F({2.5 , 3.5, 1:5})ans(:,:,1) =      0.59227ans(:,:,2) =      0.16977ans(:,:,3) =      0.26829ans(:,:,4) =      0.58663ans(:,:,5) =       0.6637
dbstop if error