Smoothstep is a family of sigmoid-like interpolation and clamping functions commonly used in computer graphics,[1][2] video game engines,[3] and machine learning.
The function receives a real number x as an argument and returns 0 if x is less than or equal to the left edge, 1 if x is greater than or equal to the right edge, and smoothly interpolates, using a Hermite polynomial, between 0 and 1 otherwise.
The gradient of the smoothstep function is zero at both edges.
This is convenient for creating a sequence of transitions using smoothstep to interpolate each segment as an alternative to using more sophisticated or expensive interpolation techniques.
is identical to the clamping function: The characteristic S-shaped sigmoid curve is obtained with
With n = 1, the slopes or first derivatives of the smoothstep are equal to zero at the left and right edge (x = 0 and x = 1), where the curve is appended to the constant or saturated levels.
With higher integer n, the second and higher derivatives are zero at the edges, making the polynomial functions as flat as possible and the splice to the limit values of 0 or 1 more seamless.
Ken Perlin suggested[6] an improved version of the commonly used first-order smoothstep function, equivalent to the second order of its general form.
It has zero 1st- and 2nd-order derivatives at x = 0 and x = 1: C/C++ reference implementation: Starting with a generic third-order polynomial function and its first derivative: Applying the desired values for the function at both endpoints: Applying the desired values for the first derivative of the function at both endpoints: Solving the system of 4 unknowns formed by the last 4 equations result in the values of the polynomial coefficients: This results in the third-order "smoothstep" function: Starting with a generic fifth-order polynomial function, its first derivative and its second derivative: Applying the desired values for the function at both endpoints: Applying the desired values for the first derivative of the function at both endpoints: Applying the desired values for the second derivative of the function at both endpoints: Solving the system of 6 unknowns formed by the last 6 equations result in the values of the polynomial coefficients: This results in the fifth-order "smootherstep" function: Applying similar techniques, the 7th-order equation is found to be: Smoothstep polynomials are generalized, with 0 ≤ x ≤ 1 as where N determines the order of the resulting polynomial function, which is 2N + 1.
that transition from 0 to 1 when x transitions from 0 to 1 can be simply mapped to odd-symmetry polynomials where and The argument of RN(x) is −1 ≤ x ≤ 1 and is appended to the constant −1 on the left and +1 at the right.
in Javascript:[7] The inverse of smoothstep() can be useful when doing certain operations in computer graphics when its effect needs to be reversed or compensated for.
The series expansion of the inverse, on the other hand, does not terminate.