Interleaved Sampling

Introduction

On this web page we illustrate the quality benefits of interleaved sampling more clearly than this is possible in a printed document. Especially in the volume rendering part we also provide some additional material that is not in the printed Rendering Workshop paper.

Overview:

Abstract

The sampling of functions is one of the most fundamental tasks in computer graphics, and occurs in a variety of different forms. The known sampling methods can roughly be grouped in two categories. Sampling on regular grids is simple and efficient, and the algorithms are often easy to built into graphics hardware. On the down side, regular sampling is prone to aliasing artifacts that are expensive to overcome. Monte Carlo methods, on the other hand, mask the aliasing artifacts by noise. However, due to the lack of coherence known from regular grids, and are more expensive and not well suited for hardware implementations.

In this paper, we introduce a novel sampling scheme where samples from several regular grids are a combined into a single irregular sampling pattern. The relative positions of the regular grids are themselves determined by Monte Carlo methods. This generalization obtained by interleaving yields significantly improved quality compared to traditional approaches while at the same time preserving much of the advantageous coherency of regular sampling.

We demonstrate the quality of the new sampling scheme with a number of applications ranging from supersampling over effects like shadow maps, depth-of-field and motion blur to volume rendering. Due to the coherence in the interleaved samples, the method is optimally suited for implementations in graphics hardware.