# What is the difference between FC and MLP in as used in PointNet?

I am trying to understand the PointNet network for dealing with point clouds and struggling with understanding the difference between FC and MLP:

"FC is fully connected layer operating on each point. MLP is multi-layer perceptron on each point."

I understand how fully connected layers are used to classify and I previously thought, was that MLP was the same thing but it seems varying academic papers have a differing definition from each other and from general online courses. In PointNet what is meant by a shared MLP different to a standard feedforward fully connected network?

As an example, let $$f_\theta$$ be an MLP with parameters $$\theta$$. Say we have a 3D point cloud $$[\vec{x}_1,\ldots,\vec{x}_n]\subseteq \mathbb{R}^3$$. If we apply $$f_\theta$$ as a shared MLP in the way PointNet describes, the result would be $$[f_\theta(\vec{x}_1),\ldots,f_\theta(\vec{x}_n)]$$.