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The SGGX Microflake Distribution

Eric Heitz, Jonathan Dupuy, Cyril Crassin, and Carsten Dachsbacher

ACM Transactions on Graphics (Proceedings of SIGGRAPH 2015), to appear

Extract
Top-left: rendering a voxelized forest at decreasing levels of details (left to right). Bottom-right: visualization of the voxel structure at the matching resolutions. We use the SGGX microflake distribution to represent volumetric anisotropic materials. Our representation supports downscaling and interpolation, resulting in smooth and antialiased renderings at multiple scales.
Abstract
We introduce the Symmetric GGX (SGGX) distribution to represent spatially-varying properties of anisotropic microflake participating media. Our key theoretical insight is to represent a microflake distribution by the projected area of the microflakes. We use the projected area to parameterize the shape of an ellipsoid, from which we recover a distribution of normals. The representation based on the projected area allows for robust linear interpolation and prefiltering, and thanks to its geometric interpretation, we derive closed form expressions for all operations used in the microflake framework. We also incorporate microflakes with diffuse reflectance in our theoretical framework. This allows us to model the appearance of rough diffuse materials in addition to rough specular materials. Finally, we use the idea of sampling the distribution of visible normals to design a perfect importance sampling technique for our SGGX microflake phase functions. It is analytic, deterministic, simple to implement, and one order of magnitude faster than previous work.