Multiple Scattering in Inhomogeneous Participating Media Using Rao‐Blackwellization and Control Variates

Abstract

Rendering inhomogeneous participating media requires a lot of volume samples since the extinction coefficient needs to be integrated along light paths. Ray marching makes small steps, which is time consuming and leads to biased algorithms. Woodcocklike approaches use analytic sampling and a random rejection scheme guaranteeing that the expectations will be the same as in the original model. These models and the application of control variates for the extinction have been successful to compute transmittance and single scattering but were not fully exploited in multiple scattering simulation. Our paper attacks the multiple scattering problem in heterogeneous media and modifies the light–medium interaction model to allow the use of simple analytic formulae while preserving the correct expected values. The model transformation reduces the variance of the estimates with the help of Rao‐Blackwellization and control variates applied both for the extinction coefficient and the incident radiance. Based on the transformed model, efficient Monte Carlo rendering algorithms are obtained.

Efficient Simulation of Light Scattering Effects in the Atmosphere

“…In order to more realistically reproduce light scattering effects, significant progress has also been made in the study of multiple scattering. László et al [14] improved the traditional light-medium interaction model, allowing control of the extinction coefficient and control variables through approximated sampled values, thereby enhancing rendering efficiency. In 2019, Vibert et al [15] presented a new scalable hierarchical VRL method that preferentially samples VRLs according to their image contribution, yet this method requires further improvement for rendering anisotropic media.…”

Rendering Discrete Participating Media with Geometrical Optics Approximation

“…Rendering participating media is a challenging but important problem, which requires efficiently solving the VRE [1], [3] by means of Monte Carlo path integration [2], [20], [21], [22], [23], photon density estimation [24], [25], [26], [27], [28], [29], [30], or a combination of both [31]. In our current framework, we choose Monte Carlo path integration by virtue of its elegant simplicity, generality, and accuracy.…”