Since Matlab cant handle tga files, I convert TGA files into BMP files on the fly using ImageMagick's convert commandline tool.
Hence this code will need linux machine with "convert" command available.
Code: readTGA.m
For calibrating the direction of the light using the chrome ball, I
- Determined the center of the ball by averaging all pixels in mask with value=255, say [xc yc]
- Similarly determined the center of the bright spot, say [xl yl]
- Determined the radius by averaging the distance between points with value in mask greater than 0 but less than 255(boundary points). Say r.
- N = [xl-xc yl-yc sqrt(r^2 - (xl-xc)^2 - (yl-yc)^2)] normalized to norm=1. R=[0 0 -1]
- L = 2*N*R' - R
For detemining the normals, I
- For each pixel in ever image, combine the values for each channel.
- Try to minimize the objective function, (w*I - w*L*g) where w = I.
- I even attempted using the hat function as the weight just like the one used in HDR, but that had almost no impact on the output.
- N = (1/norm(g))*g. k = norm(g).
For obtaining the color albedo, I used the equation I.J/J.J as given on the project page. This seems to work well.
Code: getAlbedo.m
For solving this problem, I
- Created an index of pixel co-ordinates where mask value is non-zero.
- Created two sparse matrices A and b
- A and b contain rows corresponding to gradient in each direction as per the equations on the project page. Since every row in A has only two non-zero entries, the matrix is very sparse.
- A has one row to fix one pixel to a height > 0 so that the entire surface appears to be above 0.
Code: computeZ.m
There were multiple small changes that I attempted.
- Used a hat weighing function for the normal computation. Causes
some peaks - look at the eyes.
- Used no weighing function for the normal computation. Still doesn't get rid off the peaks.
- Used a hat weighing function for surface estimation. So every
row in the matrix A and b was scaled by the intensity. Creates wierd
peaks in the image.
- Used the intensity itself as the weighing function. This also
creates peaks in the image.
- To reduce the peaks in the surface, I added another
minimization term to minimize the difference between the adjacent
points i.e. minimize the sum of squared differences between
neighbouring pixels. This still keeps A sparse.
This works well to reduce noise. This is what I have used in all the output images. - I used MATLAB's builtin "pcg" function to solve the surface
estimation equation with A'Az = A'b.
The result was not very different from the output given by \.
NOTE: They both use the squared difference terms mentioned in the previous point. Also PCG with 100 iterations gave a very noisy answer, so I used 1000 iterations.With \ With pcg (1000 iterations) 
- Sometimes, the Z-values gradually increase to high
values. Hence, I tried adding a constraint to minimize the norm of
the Z values similar to HDR.
But this doesn't help much because I was raising the entire surface. Also without that condition, even if the norm is small, 10^-3 would appear as a peak if all values are 10^-3. The norm in this case would still be low.
0. Prereq:
Copy the psmImages folder into the directory that contains the code.
Make sure convert is a valid command or add /usr/bin/ to $PATH.
1. stereo
2. Various parameters that can be changed from stereo.m
- usePCG - Use PCG for solving surface estimation.
- useSquaredDiff - Use the squared differences between adjacent Z values.
- useWeightsSurf - Use weights for estimating the surface in computeZ.m
- useWeightsNormal - Use weights for estimating the normal in getNormals.m
| DataSet | RGB-encoded Normals(absolute values) | Albedo-map | Surface |
| Buddha |
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| Owl |
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| Horse |
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| Rock |
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| Gray |
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| Cat |
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