spectrally optimal sampling for distribution ray tracing
Nonuniform sampling of images is a useful technique in computer graphics, because a properly designed pattern of samples can make aliasing take the form of high-frequency random noise. In this paper, the technique of nonuniform sampling is extended from two dimensions to include the extra parameter dimensions of distribution ray tracing. A condition for optimality is suggested, and algorithms for approximating optimal sampling are developed. The technique is demonstrated at low sampling densities, so the characteristics of aliasing noise are clearly visible. At supersampling rates, this technique should move noise into frequencies above the passband of the pixel-reconstruction filter.
optimality sampling distributed distribution ray tracing ray tracing nonuniform sampling imaging computer graphics pattern aliasing randomization noise paper algorithm approximation filtering reconstruction antialiasing noise perception