Voronoi integration of the rendering equation

TL;DR

This paper introduces a Voronoi tessellation reweighting scheme combined with Poisson point process sampling to improve variance control in Monte Carlo-based rendering, leading to less noisy images.

Contribution

It proposes a novel Voronoi integration method for the rendering equation that reduces variance compared to traditional Monte Carlo techniques.

Findings

Variance is lower with the Voronoi method at high process intensities.

The method is effective for functions satisfying Holder continuity.

The approach improves rendering quality in challenging regions.

Abstract

In photorealistic image rendering, Monte Carlo methods form the foundation for the integration of the rendering equation in modern approaches. However, despite their effectiveness, traditional Monte Carlo methods often face challenges in controlling variance, resulting in noisy visual artifacts in regions that are difficult to render. In this work, we propose a new approach to the integration of the rendering equation by introducing a Voronoi tessellation reweighting scheme combined with a Poisson point process sampling strategy to address some of the limitations of standard Monte Carlo methods. From a theoretical point of view, we show that the variance induced by a Poisson-Voronoi tessellation is smaller than that of the Monte Carlo method when the intensity of the underlying process is arbitrarily large and when the function to be integrated satisfies a Holder continuity condition.