三维重建是怎么做的？（未解决）

1、方式一：matlab2014a

%% Reconstruct the 3-D Scene

% Reconstruct the 3-D world coordinates of points corresponding to each
% pixel from the disparity map.
pointCloud = reconstructScene(disparityMap, stereoParams);


% Convert from millimeters to meters.
pointCloud = pointCloud / 1000;
%% Visualize the 3-D Scene
%Plot the 3-D points by calling the plot3 function. We have to call the
%function in a loop for each color, because it can only plot points of a
%single color at a time. To minimize the number of iteration of the loop,
%reduce the number of colors in the image by calling rgb2ind.


% Reduce the number of colors in the image to 128.
[reducedColorImage, reducedColorMap] = rgb2ind(J1, 128);


%这里的坐标是怎么回事？840 630？
% Plot the 3D points of each color.
hFig = figure; hold on;
set(hFig, 'Position', [1 1 840   630]);
hAxes = gca;


X = pointCloud(:, :, 1);
Y = pointCloud(:, :, 2);
Z = pointCloud(:, :, 3);


for i = 1:size(reducedColorMap, 1)
    % Find the pixels of the current color.
    x = X(reducedColorImage == i-1);
    y = Y(reducedColorImage == i-1);
    z = Z(reducedColorImage == i-1);


    if isempty(x)
        continue;
    end


    % Eliminate invalid values.
    idx = isfinite(x);
    x = x(idx);
    y = y(idx);
    z = z(idx);


    % Plot points between 3 and 7 meters away from the camera.
    maxZ = 7;
    minZ = 3;
    x = x(z > minZ & z < maxZ);
    y = y(z > minZ & z < maxZ);
    z = z(z > minZ & z < maxZ);


    plot3(hAxes, x, y, z, '.', 'MarkerEdgeColor', reducedColorMap(i, :));
    hold on;
end


% Set up the view.
grid on;
cameratoolbar show;
axis vis3d
axis equal;
set(hAxes,'YDir','reverse', 'ZDir', 'reverse');

camorbit(-20, 25, 'camera', [0 1 0]);

2、方式二：matlabs2013a

%% Step 7. Backprojection
% % With a stereo depth map and knowledge of the intrinsic parameters of the
% % camera, it is possible to backproject image pixels into 3D points [1,2].
% % One way to compute the camera intrinsics is with the MATLAB Camera
% % Calibration Toolbox [6] from the California Institute of Technology(R).
% % Such a tool will produce an intrinsics matrix, K, of the form:
% %
% %  K = [focal_length_x          skew_x  camera_center_x;
% %                    0  focal_length_y  camera_center_y;
% %                    0               0                1];
% %
% % This relates 3D world coordinates to homogenized camera coordinates via:
% %
% % $$[x_{camera} \, y_{camera} \, 1]^T = K \cdot [x_{world} \, y_{world} \, z_{world}]^T$$
% %
% % With the intrinsics matrix, we can backproject each image pixel into a 3D
% % ray that describes all the world points that could have been projected
% % onto that pixel on the image. This leaves unknown the distance of that
% % point to the camera. This is provided by the disparity measurements of
% % the stereo depth map as:
% %
% % $$z_{world} = focal\_length \cdot \frac{1 + stereo\_baseline}{disparity}$$
% %
% % Note that unitless pixel disparities cannot be used directly in this
% % equation. Also, if the stereo baseline (the distance between the two
% % cameras) is not well-known, it introduces more unknowns. Thus we
% % transform this equation into the general form:
% %
% % $$z_{world} = a + \frac{b}{disparity}$$
% %
% % We solve for the two unknowns via least squares by collecting
% % a few corresponding depth and disparity values from the scene and using
% % them as tie points. The full technique is shown below.


% Camera intrinsics matrix
% K = [409.4433         0  204.1225
%             0  416.0865  146.4133
%             0         0    1.0000];


%20140427用matlab2014a的标定函数生成的内参矩阵，为啥重建不对？
K = [23851.8619753065,0,0;0,23561.6461045461,0;591.522934472755,430.985873195448,1];
% Create a sub-sampled grid for backprojection.
dec = 2;
[X,Y] = meshgrid(1:dec:size(leftI,2),1:dec:size(leftI,1));
P = K\[X(:)'; Y(:)'; ones(1,numel(X), 'single')];
Disp = max(0,DdynamicSubpixel(1:dec:size(leftI,1),1:dec:size(leftI,2)));
hMedF = vision.MedianFilter('NeighborhoodSize',[5 5]);
Disp = step(hMedF,Disp); % Median filter to smooth out noise.
% Derive conversion from disparity to depth with tie points:
knownDs = [15   9   2]'; % Disparity values in pixels
knownZs = [4  4.5 6.8]';
% World z values in meters based on scene measurements.
ab = [1./knownDs ones(size(knownDs), 'single')] \ knownZs; % least squares
% Convert disparity to z (distance from camera)
ZZ = ab(1)./Disp(:)' + ab(2);
% Threshold to [0,8] meters.
ZZdisp = min(8,max(0, ZZ ));
Pd = bsxfun(@times,P,ZZ);
% Remove near points
bad = Pd(3,:)>8 | Pd(3,:)<3;
Pd = Pd(:,~bad);




% In the reprojection, the walls, ceiling, and floor all appear mutually
% orthogonal, and the scene is well reconstructed. Since camera calibration
% also gives intrinsics with units, we can assign units to the
% backprojected points. The dimensions of the plot are given in meters, and
% one can verify that the sizes of objects and the scene appear correct.


% Collect quantized colors for point display
Colors = rightI3chan(1:dec:size(rightI,1),1:dec:size(rightI,2),:);
Colors = reshape(Colors,[size(Colors,1)*size(Colors,2) size(Colors,3)]);
Colors = Colors(~bad,:);
cfac = 20;
C8 = round(cfac*Colors);
[U,I,J] = unique(C8,'rows');
C8 = C8/cfac;


figure(2), clf, hold on, axis equal;
for i=1:size(U,1)
    plot3(-Pd(1,J==i),-Pd(3,J==i),-Pd(2,J==i),'.','Color',C8(I(i),:));
end
view(161,14), grid on;
xlabel('x (meters)'), ylabel('z (meters)'), zlabel('y (meters)');