CS计算机代考程序代写 function [ Jac, residual ] = deriveAnalytic( IRef, DRef, I, xi, K )

function [ Jac, residual ] = deriveAnalytic( IRef, DRef, I, xi, K )
% calculate analytic derivative

% get shorthands (R, t)
T = se3Exp(xi);
R = T(1:3, 1:3);
t = T(1:3,4);

% ========= warp pixels into other image, save intermediate results ===============
% these contain the x,y image coordinates of the respective
% reference-pixel, transformed & projected into the new image.
nImg = zeros(size(IRef))-10;
mImg = zeros(size(IRef))-10;

% these contain the 3d position of the transformed point
xp = NaN(size(IRef));
yp = NaN(size(IRef));
zp = NaN(size(IRef));
for n=1:size(IRef,2)
for m=1:size(IRef,1)
% TODO warp points into target frame
end
end

% ========= calculate actual derivative. ===============
% 1.: calculate image derivatives, and interpolate at warped positions.
% TODO image gradient in x and y direction using central differences
%dxI = …
%dyI = …
% interpolate at warped positions
Ixfx = K(1,1) * reshape(interp2(dxI, nImg+1, mImg+1),size(I,1) * size(I,2),1);
Iyfy = K(2,2) * reshape(interp2(dyI, nImg+1, mImg+1),size(I,1) * size(I,2),1);

% 2.: get warped 3d points (x’, y’, z’).
xp = xp(:);
yp = yp(:);
zp = zp(:);

% 3. implement gradient computed in Theory Ex. 1 (b)
Jac = zeros(size(I,1) * size(I,2),6);
% TODO implement analytic partial derivatives
%Jac(:,1) = …
%Jac(:,2) = …
%Jac(:,3) = …
%Jac(:,4) = …
%Jac(:,5) = …
%Jac(:,6) = …

% ========= plot residual image =========
residual = interp2(I, nImg+1, mImg+1) – IRef;
imagesc(residual), axis image
colormap gray
set(gca, ‘CLim’, [-1,1])
residual = residual(:);
end