3D Photos - Civil War reenactors

Left image (after rectification).

Right image (after rectification).

I took the pictures with my Fuji W3, opened the mpo in StereoPhoto Maker, reduced the size to 1200 (in width), and rectified the reduced-size stereo pair with ER9b. I think I could have gone without the rectification process but ER9b gave me the min and max disparities, which is nice (no need to use DF2 to find those out manually).

Time to get the depth/disparity map. First, I am gonna use DMAG5.

Left depth map obtained by DMAG5.

Input for DMAG5:

min disparity = -22
max disparity = 19
radius = 16
alpha = 0.9
truncation cost (color) = 20.0
truncation cost (gradient) = 10.0
epsilon = 4
disparity tolerance = 0
radius (occlusion smoothing) = 9
sigma space (occlusion smoothing) = 9.0
sigma color (occlusion smoothing) = 25.5
downsampling factor = 1

I guess one could play around with the radius to get different depth maps but this one looks pretty good to me out of the box. There's no need to play with any of the other parameters.

Now, let's get the depth/disparity map with DMAG6.

Left depth map obtained by DMAG6.

Input for DMAG6:

min disparity = -22
max disparity = 19
alpha = 0.9
truncation cost (color) = 20.
truncation cost (gradient) = 10.
truncation cost (discontinuity) = 10000.
iteration number = 5
level number = 5
data cost weight = 0.5
disparity tolerance = 0
radius (occlusion smoothing) = 9
sigma space (occlusion smoothing) = 9.0
sigma color (occlusion smoothing) = 25.5
downsampling factor = 1

I guess one could play with the data cost weight to try to possibly get a better depth although this one looks pretty good. When the data cost weight is reduced, the depth map becomes smoother but there is a danger that it may become too smooth and not accurate enough. When the data cost weight is increased, the depth map becomes more accurate but less smooth (it might be a good idea to post-process the depth map with EPS7, EPS9 or DMAG9b to regain some smoothness). There's no need to play with any of the other parameters.

Not a whole lot of difference between the depth map produced by DMAG5 and the one produced by DMAG6, so I used the depth map produced by DMAG5 in what follows. It's relatively amazing to see that there's not a whole lot of difference between the depth map produced by DMAG5, a local window-based method, and DMAG6, a global method that's based on Belief Propagation (BP).

Animated gif courtesy of "Wiggle Maker".