Efficient Supersampling Antialiasing for High-Performance Architectures

Abstract : Techniques are presented for increasing the efficiency of supersampling antialiasing in high-performance graphics architectures. The traditional approach is to sample each pixel with multiple, regularly spaced or jittered samples, and to blend the sample values into a final value using a weighted average. This paper describes a new type of antialiasing kernel that is optimized for the constraints of hardware systems and produces higher quality images with fewer sample points than traditional methods. The central idea is to computer a Poisson-disk distribution of sample points for a small region of the screen (typically pixel-sized, or the size of a few pixels). Sample points are then assigned to pixels so that the density of samples points (rather than weights) for each pixel approximates a Gaussian (or other) reconstruction filter as closely as possible. The result is a supersampling kernel that implements importance sampling with Poisson-disk-distributed samples. The method incurs no additional run-time expense over standard weighted-average supersampling methods, supports successive-refinement, and can be implemented on any high- performance system that point samples accurately and has sufficient frame-buffer storage for two color buffers.