I’m not sure I understand what you’re after enough to tell which is the best approach.
So you have an array 512,512,30 points and labels of x,y,z which are the centers of the objects of interest? You don’t have label information about the “radius”/Variance but want to extract that from the image?
I’m asking because to make an image with Gaussians of the same variance around given x,y,z, you could just use scatter + convolution, but that doesn’t appear what you are after. How many points do you have 1, 10, 100? What is the range? Are all brightnesses the same? Or in other words if we consider superposition of gaussians, would they all have the same coefficient? Is it homogenous in the coordinates (i.e. circles rather than ellipses)?
To me it sounds like the first order of business might be estimating the covariance matrices and potentially weights. You could try to do that by creating a coordinate grid and superimposing the gaussians and minimize some error function, but I’m not sure it would work.
Best regard
Thomas