### Intro

One of the basic building blocks of impicit procedural modeling (such as when building a distance field for raymarching based on basic primitives) is the union operator.

float opU( float d1, float d2 )
{
return min( d1, d2 );
}

This operator works great, but has the problem that the resulting shape has discontinuities in its derivatives. Or in other words, the resulting surface of unifying two smooth objects is not a smooth surface anymore. This is often inconvenient from a looks perspective, such as when trying to model organic shapes.

*Regular min() based primitive union*

*smooth-min() based primitive union*

### Several implementations

The way to smoothly blend the shapes is to get rid of the discontinuity of the min() function, of course. But we want our smooth-min function to behave quite like min() when one of the two primitives is way further that the other. It's only in the area where the two values get similar that we want to apply the smoothness.

// exponential smooth min (k = 32);
float smin( float a, float b, float k )
{
float res = exp2( -k*a ) + exp2( -k*b );
return -log2( res )/k;
}

// polynomial smooth min (k = 0.1);
float smin( float a, float b, float k )
{
float h = clamp( 0.5+0.5*(b-a)/k, 0.0, 1.0 );
return mix( b, a, h ) - k*h*(1.0-h);
}

// power smooth min (k = 8);
float smin( float a, float b, float k )
{
a = pow( a, k ); b = pow( b, k );
return pow( (a*b)/(a+b), 1.0/k );
}

*k*that controls the radious/distance of the smoothness. From these three, probably the polynomial is the fastest, and also the easiest to control, for

*k*maps directly to a blending band size/distance. Unlike the other two, it probably suffers from second order discontinuities (derivatives), but visually is pleasing enough for most applications.

As a curiosity, it might be worth noting that the exponential and power based smooth-min functions, both generalize to more than two distances, so they are probably better suited for computing minimun distances to big sets of points beyond 2, for example when you want to compute smooth voronoi patterns or interpolate pointclouds. In the case of the power based smooth-min function, the expression

*a*b/(a+b)*generalizes with the same formula as when computing the global resistance of N parallel resistors: 1/ ( 1/a + 1/b + 1/c + ... ). For example, for three distances, you get a*b*c / (b*c + c+a + a*b).

Besides accepting an aribtrary number of points, another advantage of the exponential smin() over the polynomial smin() is that when called multiple times with two arguments at a time, the exponential smin() produces the same result regardless of the order of the operations. The polynomial smin() however is not order independent. To make it more explicit, smin(a,smin(b,c)) is equal to smin(b,smin(a,c)) for the exponential smin(), but not for the polynomial. That means that the exponential smin() allows one to process long lists of distances in any arbitrary order and slowly compute the smin, while the polynomial is ordering dependent.

EDIT 5 years later - I have learnt that Media Molecule used the polynomial smin() presented here for their game "Dreams" (credited to Dave Smith), although their rewrote it in an equivalent but more efficient form to look like this:

// polynomial smooth min (k = 0.1);
float smin( float a, float b, float k )
{
float h = max( k-abs(a-b), 0.0 )/k;
return min( a, b ) - h*h*k*(1.0/4.0);
}

As noted by Shadertoy user TinyTexel, this can be generalized to higher levels of continuity than the quadratic polynomail offers (C1), which might be important for preventing lighting artifacts. Moving on to a cubic curve gives us C2 continuity, and doesn't get a lot more expensive than the quadratic one anyways:

// polynomial smooth min (k = 0.1);
float sminCubic( float a, float b, float k )
{
float h = max( k-abs(a-b), 0.0 )/k;
return min( a, b ) - h*h*h*k*(1.0/6.0);
}

Laslty, it's worth mentioning that since the polynomial smin() is always smaller or equal to regular min() by design, it is well suited for raymarching algorithms since the ray will never overshoot past the original sharp intersection.

*Two functions*

*The polynomial smooth min of the two funcitons*

### Derivation

Deriving the polynomial smin() fuction is not difficult. The easiest is probably to start with the simplified version, and opearte in 1 dimension to makes things easier (the generalization to 2 or more dimensions or trivial):

smin( f(x), g(x), k ) = min( f(x), g(x) ) - w(x,k)

If x=a is the point at which f(x)-g(x) = -k, and x=b is the point where f(x)-g(x) = k, and a middle point x=c is where f(x)=g(x), then we know that

w(a)=0, w(b)=0, w(c)=s

with s being the maximum value. Since we want w(x) to be a smooth function that connects nicely to the curves f(x) and g(x) at the points x=a and x=b, we can choose

w(x) = s·hⁿ(x)

with h(x) = 1 ± [f(x)-g(x)]/k

meaning h(x) is a power curve of degree n, with range between 0 and 1, and with the sings being positive if x < c and negative if x > c. This gives two versions of w(x), which we can call wl(x) and wr(x) for "left" and "right".

Since we want continuity also at x=c, we need to make sure that the derivatives of smin match when coming from either left or right of x=c. Therefore,

f'(c) + wl'(c) = g'(c) + wr'(c)

which means that

f'(c) + [f'(c)-g'(c)]nS/k = g'(c) - [f'(c)-g'(c)]·n·s/k

This can only be solved if 1+2·n·s/k = 0, which gives

s = -k/2n

which is what we used in the quadratic and cubic implementations above.

The quadratic smin() though doesn't have second derivatives for w(x), meaning tha the only way the condition

f''(c) + wl''(c) = g''(c) + wr''(c)

can be met is making w(x) a cubic. Fortunately, since h(c)=1, the same condition 1+2·n·s/k = 0 needs to be met for ensuring continuity of the second (or any higher order) derivative.

### Sumary

Properties of all the most useful smooth-minimum functions:

Quadratic:

- smin(a,b,k) = min(a,b) - h²/(4k)

h = max( k-abs(a-b), 0 ) - Continity: C1
- Order Independent: No
- Generalized: No

Cubic:

- smin(a,b,k) = min(a,b) - h³/(6k²)

h = max( k-abs(a-b), 0) - Continity: C2
- Order Independent: No
- Generalized: No

Exponential:

- smin(a,b,k) = -ln( e
^{-k⋅a}+ e^{-k⋅b})/k - Continity: C-inf
- Order Independent: Yes
- Generalized: Yes

### Results

In general, the polynomial smooth-min function works very well, predictably and fast. It can be used to make surfaces connect, such as snow and archnitecture in the images below.

*Regular min() based primitive union*

*Polynomial smooth-min() based primitive union*

It obviously becomes very handy for connecting the different pieces of one same character, such as the fingers, hands, arms and body (wich in this case are made of cylinder/cones for the fingers and arms and a sphere for the body). The image bellow is interactive, and you can click and move the mouse over it to see the effect of the polynomial smooth-min function compared to the regular min function.

And finally, this is the result under animation of the body parts that are blended (note that texture coordinates are not fixed so the texture swims, but that's a different problem unrelated to the smooth-min, which works beautifully also with moving objects):