Improving the Dwivedi Sampling Scheme

Abstract

Despite recent advances in Monte Carlo rendering techniques, dense, high-albedo participating media such as wax or skin still remain a difficult problem. In such media, random walks tend to become very long, but may still lead to a large contribution to the image. The Dwivedi sampling scheme, which is based on zero variance random walks, biases the sampling probability distributions to exit the medium as quickly as possible. This can reduce variance considerably under the assumption of a locally homogeneous medium with constant phase function. Prior work uses the normal at the Point of Entry as the bias direction. We demonstrate that this technique can fail in common scenarios such as thin geometry with a strong backlight. We propose two new biasing strategies, Closest Point and Incident Illumination biasing, and show that these techniques can speed up convergence by up to an order of magnitude. Additionally, we propose a heuristic approach for combining biased and classical sampling techniques using Multiple Importance Sampling.