3D MATLAB noise – effect of changing Gaussian convolution kernel size

To illustrate the effect of changing the Gaussian convolution kernel size, I generated a series of 64x64x64 3D noise texture arrays using the code from my 3D MATLAB noise (continued) post:

After the break, see how increasing the size of the convolution kernel affects the quality of the 3D noise.

Note that “Time to Process” was calculated tic and toc (see above) on a quad-core Xeon W3530 @ 2.80GHz with 12 GB of RAM. In addition, the 2D FFT animations were generated using each frame of the GIF animations using the following code:

k Time to Process 2D FFT
1 0.4167
3 0.8670
5 2.2663
7 5.7784
9 11.2293
11 20.8108
13 33.3277
15 52.0085
17 82.3220
19 187.6235
21 397.4730
23 615.1934
25 852.5891
27 1190.7
29 1641.1
31 1822.3

Subjectively, there is diminishing return as the convolution kernel increases past 13-15 pixels. Objectively, it makes little sense to spend the computational time processing past 15:

Here is a MAT file with these 3D noise arrays:
noise3Dwrap64_k1_31.mat

One thought to “3D MATLAB noise – effect of changing Gaussian convolution kernel size”

  1. Hi, Steven!

    Wouldn’t the running time here slowed down heavily by calls to the file system, and the hard drive recording, since you save it immediately at each step?

    Cheers,
    Leonid

    P.S. Very cool blog, I found it by looking how to generate “blobify” surfaces with sinusoidal transformations over parametric equations.

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