[siddboots]

A Gaussian process fits a single high dimensional Gaussian, for example, by treating n observations along a single dimension as a n dimensional space.

Gaussian mixture models fit a large number of low dimensional Gaussians for example you might imagine 2D data generated by several 2D Gaussian superimposed.

This approach is just an example of the latter. It uses higher dimensional Gaussians to capture extra information from a scene, but not in the emulation of an infinite dimensional space in the way that defines Gaussian processes.

[abhgh]

To add to a sibling comment, if you're interested in learning a bit about the both the Gaussian (as in a density estimator like Gassian Mixture Models, aka GMMs) vs Gaussian Processes (GP), I have some write-ups here: [1] and [2].

[1] Fun with GMMs https://blog.quipu-strands.com/fun_with_GMMs

[2] This is a larger article on BayesOpt, but I've a section dedicated to GPs: https://blog.quipu-strands.com/bayesopt_1_key_ideas_GPs#gaus...