Real-time rendering of procedural multiscale materials

Many real‐life materials have a sparkling appearance. Examples include metallic paints, sparkling fabrics and snow. Simulating these sparkles is important for realistic rendering but expensive. As sparkles come from small shiny particles reflecting light into a specific direction, they are very challenging for illumination simulation. Existing approaches use a four‐dimensional hierarchy, searching for light‐reflecting particles simultaneously in space and direction. The approach is accurate, but extremely expensive. A separable model is much faster, but still not suitable for real‐time applications. The performance problem is even worse when illumination comes from environment maps, as they require either a large sample count per pixel or pre‐filtering. Pre‐filtering is incompatible with the existing sparkle models, due to the discrete multi‐scale representation. In this paper, we present a GPU‐friendly, pre‐filtered model for real‐time simulation of sparkles and glints. Our method simulates glints under both environment maps and point light sources in real time, with an added cost of just 10 ms per frame with full high‐definition resolution. Editing material properties requires extra computations but is still real time, with an added cost of 10 ms per frame.

Section: Resultsmentioning

confidence: 99%

Real‐Time Glints Rendering With Pre‐Filtered Discrete Stochastic Microfacets

Wang

Deng

Holzschuch

2020

“…Bowles and Wang [BW15,WB16] introduced a procedural sparkle approach for snow; this technique has been used in production. Zirr et al [ZK16] derived a stochastic biscale microfacet model to fit for real-time application. All these algorithms are faster than ours, but are not physically based.…”

Section: Real-time Glint Renderingmentioning

confidence: 99%

Fast Global Illumination with Discrete Stochastic Microfacets Using a Filterable Model

Wang

Holzschuch

2018

“…Since these models usually adopt a smooth NDF to describe the microfacet orientations, they are only valid for very distant views, assuming that the micro‐scale details are indistinguishable [YHJ*14]. Capturing high‐frequency glints resulting from irregular microscopic surface structures requires either a discrete NDF [JHY*14] or a multi‐scale NDF [ZK16].…”

Section: Related Workmentioning

confidence: 99%

“…Our model is based on a similar idea, and thus can be viewed as an extension to this model. Zirr et al [ZK16] derived a stochastic biscale microfacet model to fit for real‐time application. Data‐driven models [RMS*08, DWMG15] that use measured data from real‐world materials exhibiting high‐frequency surface details can also generate vivid sparkling effects.…”

Section: Related Workmentioning

confidence: 99%

A Physically‐based Appearance Model for Special Effect Pigments

Guo

Chen

Guo

et al. 2018

An appearance model for materials adhered with massive collections of special effect pigments has to take both high‐frequency spatial details (e.g., glints) and wave‐optical effects (e.g., iridescence) due to thin‐film interference into account. However, either phenomenon is challenging to characterize and simulate in a physically accurate way. Capturing these fascinating effects in a unified framework is even harder as the normal distribution function and the reflectance term are highly correlated and cannot be treated separately. In this paper, we propose a multi‐scale BRDF model for reproducing the main visual effects generated by the discrete assembly of special effect pigments, enabling a smooth transition from fine‐scale surface details to large‐scale iridescent patterns. We demonstrate that the wavelength‐dependent reflectance inside the pixel's footprint follows a Gaussian distribution according to the central limit theorem, and is closely related to the distribution of the thin‐film's thickness. We efficiently determine the mean and the variance of this Gaussian distribution for each pixel whose closed‐form expressions can be derived by assuming that the thin‐film's thickness is uniformly distributed. To validate its effectiveness, the proposed model is compared against some previous methods and photographs of actual materials. Furthermore, since our method does not require any scene‐dependent precomputation, the distribution of thickness is allowed to be spatially‐varying.