Lecture 15: Advanced Topics in Material Modeling (19)

The top right case looks like a vertically brushed texture. From the p-NDF visualization, its NDF is mainly distributed in horizontal direction, which really makes sense.


I'm suddenly reminded of k-means clustering, as described in EE 16B: the direction of the cluster tells us a lot about how clean or streamlined a texture or dataset is. It would be interesting to then reverse-engineer this--to create something that takes a k-means cluster, interprets it with opacity data based on the density of points in the point cloud to mimic the fabric-like forms of the p-NDF, and then produces a normal map to yield a material representative of data sampled from an entirely different dimension (audio, text, pressure, weather data, strange modes of human input, etc.). Would such an inversion be possible with a probabilistic model like this one?

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