We present a physically based method to render the atmosphere of a planet from ground to space view. Our method is cheap to compute and, as compared to previous methods, does not require high dimensional look up tables (LUT), thus does not suffer from visual artefacts associated with them, and can approximate an infinite amount of multi-scattering bounce. We take a new look at what it means to render natural atmospheric effects and propose a set of simple look up tables and parameterizations to render a sky and its aerial perspective. The atmosphere composition can change dynamically to match artistic visions without requiring heavy LUTs update. The complete technique can be used for real-time applications such as games or architecture pre-visualizations. It scales from low power mobile platforms to high end GPU PCs and it is also useful to accelerate path tracing.
Cheng Zhang, Shuang Zhao
Many real-world materials such as sand, snow, salt, and rice are comprised of large collections of grains. Previously, multi-scale rendering of granular materials requires precomputing light transport per grain and has difficulty in handling materials with continuously varying grain properties. Further, existing methods usually describe granular materials by explicitly storing individual grains, which becomes hugely data-intensive to describe large objects, or replicating small blocks of grains, which lacks the flexibility to describe materials with grains distributed in nonuniform manners. We introduce a new method to model the appearance of granular materials with richly diverse or continuously varying grain optical properties efficiently. This is achieved using a symbolic and differentiable simulation of light transport during precomputation. Additionally, we introduce a new representation to depict large-scale granular materials with complex grain distributions. After constructing a template tile as preprocessing, we adapt it at render time to generate large quantities of grains with user-specified distributions. We demonstrate the effectiveness of our techniques using a few examples with a variety of grain properties and distributions.