Comparing cloud compare outputs

[danhps]

I'm a biologist at Adelaide University in Australia. I've been put onto Cloud Compare by some archeologist colleagues as a way to compare the shape of two anatomical structures. In my case, I am comparing the shape of the brain of an animal with the shape of its brain case, to see where the brain case shape most closely aligns with the brain shape.

I have done this for several different species, and I am wondering if there is a way to register all the heatmap clouds to each other and create an "average" heat map that shows, overall, where the brain case and brain are the most similar and most different across all the different species. Thanks for any advice!

Dan
www.DanielHoops.com

[daniel]

Do you have a screenshot of what a typical brain/heatmap corresponds to?

[danhps]

Here is one heatmap resulting from a brain case-brain comparison. I want to register a bunch of heatmaps similar to this one together and somehow "average" the values to get an average heatmap.

Screenshot from 2026-08-09 13-33-20.png (64.59 KiB) Viewed 11321 times

Dan

[daniel]

Ok, interesting. And I guess the shape of each brain changes for every animal? If it doesn't change too much, you could maybe use the 'Edit > Scalar fields > Interpolate from another entity' to transfer one set of distances from a brain mesh to another (you'll have to rename the sets of distances so that they don't clash when interpolating them).

And then you could compute the average scalar values by summing them and dividing by the right number (either manually with the 'SF Arithmetics' tool- which might be long! - or maybe look at how to automated this with Python or the command line tool if there are a lot of brains).

But the interpolation might induce some big errors if the brain shapes are quite different.

[danhps]

Thanks, Daniel! The shape doesn't change too much, but I guess it is all a matter of degrees. It sounds like it might work for two brains at least, I'll give it a try. Do you think it would work iteratively, for many brains, or would the interpolation errors compound with each additional comparison?

[daniel]

As you'll interpolate all the brains to the same one, the errors won't pile up. And in the end it might even be 'average' by definition...