I am interested to know some ways to approximate discrete gradient if you have a function on point clouds in 2D or 3D.
If you have a function defined on a grid, it well known that you can use a standard difference formula to compute the partial derivative in each direction.
I came across a paper by Luo et al entitled Approximating Gradients for Meshes and Point Clouds via Diffusion Metric which uses diffusion metric. Another idea that came into mind is to defined a smoothed field using a specific kernel, then to take the gradient of the smoothed function.
Is there a standard way of doing this?