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"represented by controllable 3D Gaussians" They just assume everyone knows what they mean with "3D Gaussian".
by cubefox 3y ago
"represented by controllable 3D Gaussians"
They just assume everyone knows what they mean with "3D Gaussian".
- drsopp 3y agoNo, they assume their peers do.
- vmfunction 3y agoIt is academia ;-)
- GaggiX 3y agoI don't think a research paper is meant to be understood by everyone, and I imagine the authors don't have that expectation either.
- data-ottawa 3y agoI actually have the same gripe, unfortunately there is a long history of academic fields naming things like this (there’s an entire Wikipedia page on this banned after Gauss/“Gaussian” https://en.wikipedia.org/wiki/List_of_things_named_after_Carl_Friedrich_Gauss https://en.wikipedia.org/wiki/List_of_things_named_after_Car...)
- blovescoffee 3y agoNot really. There are many things named after Gauss but a "Gaussian" is almost always meant to be a probability distribution / density function that is very well understood and defined (and common)
- eigenvalue 3y agoTake a linear algebra course or read a textbook before trying to read and understand cutting edge ML research!
- cubefox 3y agoRude!
- ndriscoll 3y agoSeems like they just mean the vector version of a Gaussian function: f(r) = exp(-r•r). Basically a "bell curve" except in 3D so it's a ball that's dense at the center and dies off. Then the optimizer might learn to produce an intensity, offset, and width for each point in a cloud, so the A,B,C for A*f((r-B)/C) at each point or something.
- blovescoffee 3y agoThis is in fact what the optimizer does. At least in the original paper, the model learns to skew and rotate the gaussians.