[dxbydt]

> The distribution of points in a point cloud

Would you have a canonical name for this distribution ? If you try matching log likelihoods, what parametric family does it resemble ? Briefly, given one of the canonical two dozen (uni/multi)variate distribution, one can create new distributions either by location-scale transform, mixtures, or say by using a k-param EFD family. So if I pick a k-param MVN ( multivariate normal with k means, k sigmas & O(k^2) correlations, I can create new distributions all day long by tweaking these 2k+k^2 params until cows come home. Brittle inference engines such as CNNs trained on a specific family with specific (hyper)parameters will fail once the distribution changes significantly, though visually the changes will be imperceptible.

[c0deb0t]

Sorry, I'm not too knowledgeable of the math part of the distribution. Usually, we would want some set of points from the surface of the object that maximizes the distance between each point and its nearest neighbors. Then, the points would be distributed with uniform density across the surface.

In my previous paper, I've shown that moving the points around on the surface of an object does lead to imperceptible but effective adversarial attacks, as you've observed.

[dxbydt]

no worries. On your github you have all the point clouds, so I’ll give it a shot one of these days. If you mathematize the distribution, you get a lot more mileage for your results because you get interpretation for free. Changing moments (skew etc) will sufficiently alter the dist while being imperceptible visually.