Case Study - DMAG5+DMAG9b

We are gonna use John Hooper's "People Waiting" in Saint-John, New Brunswick, to illustrate what DMAG9b can do to a depth map produced by DMAG5.

Left image rectifed by ER9c.

Right image rectified by ER9c.

Sometime, ER9b is a bit too aggressive when rectifying and you end up with quite large camera rotations (resulting in a zooming effect). In those cases, I simply switch to ER9c, like in this case.

Let's get a "basic" depth map using DMAG5.

Depth map obtained with DMAG5.

I used the following parameters in DMAG5:
radius = 16
alpha = 0.9
truncation (color) = 30
truncation (gradient) = 10
epsilon = 255^2*10^-4
disparity tolerance = 0
radius to smooth occlusions = 9
sigma_space = 9
sigma_color = 25.5
downsampling factor = 2

One could play around with the radius and epsilon to perhaps get a better depth map, but it's simpler to just let DMAG9b operate its magic.

Depth map improved by DMAG9b.

I used the following parameters in DMAG9b:
sample_rate_spatial = 32
sample_rate_range = 8
lambda = 0.25
hash_table_size = 100000
nbr of iterations (linear solver) = 25
sigma_gm = 1
nbr of iterations (irls) = 32
radius (confidence map) = 12
gamma proximity (confidence map) = 12
gamma color similarity (confidence map) = 12
sigma (confidence map) = 32

Let's change the spatial sample rate from 32 to 16, leaving everything else as is, and let's rerun DMAG9b.

Depth map improved by DMAG9b using sample_rate_spatial = 16 instead of 32.

Let's change the spatial sample rate from 16 to 8, leaving everything else as is, and let's rerun DMAG9b.

Depth map improved by DMAG9b using sample_rate_spatial = 8 instead of 16.

The depth map looks pretty good, so we are gonna stop here.

3d wobble created in wigglemaker.