Steffen

I checked the code and found out that one cannot use normalization and fixed scaling together. So, I set opt.normalize=0. however, the computed transformation seems to be far away from the optimal transformation. here is my test code:

function test_rigidTranform_noScaling( )

X = [121.3102 58.0957 46.3863
    83.3762 82.3011 69.5807
    88.7430 46.7092 84.9432
    51.3302 67.5140 109.3134];

Y = [36.7239 68.1019 52.6539
    61.0168 46.4281 88.7562
    76.5892 82.4971 84.0007
    99.3448 58.1160 121.3272];

% Set the options
opt.method='rigid'; % use rigid registration
opt.viz=0; % show every iteration
opt.outliers=0; % do not assume any noise

opt.normalize=0; % normalize to unit variance and zero mean before registering (default)
opt.scale=0; % fixed global scaling = 1
opt.rot=1; % estimate strictly rotational matrix (default)
opt.corresp=1; % compute the correspondence vector at the end of registration (default)

opt.max_it=100; % max number of iterations
opt.tol=1e-8; % tolerance

% registering Y to X
[Transform] = cpd_register(X,Y,opt);

figure,cpd_plot_iter(X, Y); title('Before');
figure,cpd_plot_iter(X, Transform.Y); title('After registering Y to X');

% compute error as mean minimum distance between points
error_before = computeError(X, Y)
error_after = computeError(X, Transform.Y)

    function [E, D] = computeError(X, Y)
        %compute for each x the euclidean distance to the closest y
        for i = 1 : size(X,1)
            D(i) = min(sqrt(sum((repmat(X(i,:), size(Y,1), 1) - Y).^2,2)));
        end
        E = mean(D);
    end
end

Any idea, what's going on? I noticed that in cpd_rigid.m the value of ntol can be negative: ntol=(L-L_old)/L; I'm not sure if this is intended. I changed it to ntol=abs((L-L_old)/L). The registration result is only slightly better, though.

Another minor thing. In cpd_register.m the line
if ~isfield(opt,'viz') || isempty(opt.tol), opt.viz = 1; end;
should be
if ~isfield(opt,'viz') || isempty(opt.viz), opt.viz = 1; end;

Best,
Steffen.