A radiative transfer framework for rendering materials with anisotropic structure


The radiative transfer framework that underlies all current rendering of volumes is limited to scattering media whose properties are invariant to rotation. Many systems allow for "anisotropic scattering," in the sense that scattered intensity depends on the scattering angle, but the standard equation assumes that the structure of the medium is isotropic. This limitation impedes physics-based rendering of volume models of cloth, hair, skin, and other important volumetric or translucent materials that do have anisotropic structure. This paper presents an end-to-end formulation of physics-based volume rendering of anisotropic scattering structures, allowing these materials to become full participants in global illumination simulations.

We begin with a generalized radiative transfer equation, derived from scattering by oriented non-spherical particles. Within this framework, we propose a new volume scattering model analogous to the well-known family of microfacet surface reflection models; we derive an anisotropic diffusion approximation, including the weak form required for finite element solution and a way to compute the diffusion matrix from the parameters of the scattering model; and we also derive a new anisotropic dipole BSSRDF for anisotropic translucent materials. We demonstrate results from Monte Carlo, finite element, and dipole simulations. All these contributions are readily implemented in existing rendering systems for volumes and translucent materials, and they all reduce to the standard practice in the isotropic case.


Supplementary material:

PDF (Expanded technical report) | Hi-res teaser image | Slides | Animated slides (Keynote, 95 MB) | Video (33 MB)

Code & the scarf dataset are available at the Mitsuba physically based renderer project.


This work was supported by the National Science Foundation (grants CCF-0347303 and CCF-0541105) and by Unilever Corporation. The authors also thank Jonathan Kaldor, Manuel Vargas and Manolis Savva for providing the scarf dataset.