# Metaballs

In computer graphics, metaballs, also known as blobby objects, are organic-looking n-dimensional isosurfaces, characterised by their ability to meld together when in close proximity to create single, contiguous objects.
In solid modelling, polygon meshes are commonly used. In certain instances, however, metaballs are superior. A metaball's "blobby" appearance makes them versatile tools, often used to model organic objects and also to create base meshes for sculpting.
The technique for rendering metaballs was invented by Jim Blinn in the early 1980s to model atom interactions for Carl Sagan's 1980 TV series Cosmos. It is also referred to colloquially as the "jelly effect" in the motion and UX design community, commonly appearing in UI elements such as navigations and buttons. Metaball behavior corresponds to mitosis in cell biology, where chromosomes generate identical copies of themselves through cell division.

## Definition

Each metaball is defined as a function in n dimensions (e.g., for three dimensions, $f(x,y,z)$; three-dimensional metaballs tend to be most common, with two-dimensional implementations popular as well). A thresholding value is also chosen, to define a solid volume. Then,
$\sum _{i}{\mbox{metaball}}_{i}(x,y,z)\geq {\mbox{threshold}}$
that is, all points larger than the threshold are inside the metaball.
As a more informal definition, consider two circles in 2D, and at point P, the influence of each circle is proportional to 1/distance from the circle's center, with the influence at P being the sum of influences. If that sum equals the threshold at point P, then P is on the surface of the metaball. If the sum is greater than the threshold, P is inside the metaball.

## Implementation

A typical function chosen for metaballs is simply inverse distance, that is, the contribution to the thresholding function falls off asymptotically toward zero the distance from the centre of the metaball increases:
    $f(r)={\frac {a}{r}}$
where $a$ scales the magnitude of the contribution (changing the radius of a metaball) and $r$ is the distance from the ball's center, calculated from cartesian coordinates as:
    $r={\sqrt {(x-x_{0})^{2}+(y-y_{0})^{2}+(z-z_{0})^{2}}}$
where $(x_{0},y_{0},z_{0})$ is the center of the metaball.
Various other falloff functions have historically been used for reasons of computational efficiency. Desirable properties of the function include:
* Finite support. A function with finite support goes to zero at a maximum radius. When evaluating the metaball field, any points beyond their maximum radius from the sample point can be ignored. Nearest neighbor search can ensure only adjacent metaballs need to be evaluated regardless of the total number in the field.
* Smoothness. Because the isosurface is the result of adding the fields together, its smoothness is dependent on the smoothness of the falloff curves.
More complicated models use an inverse square law, or a Gaussian potential constrained to a finite radius or a mixture of polynomials to achieve smoothness. The Soft Object model by the Wyvill brothers provides higher degree of smoothness.
A simple generalization of metaballs is to apply the falloff curve to distance-from-lines or distance-from-surfaces.
There are a number of ways to render the metaballs to the screen. In the case of three dimensional metaballs, the two most common are brute force raycasting and the marching cubes algorithm.
2D metaballs were a very common demo effect in the 1990s. The effect is also available as an XScreenSaver module.