Importance Sampling Microfacet‐Based BSDFs using the Distribution of Visible Normals

Heitz, Eric; d’Eon, Eugene

doi:10.1111/cgf.12417

Cited by 71 publications

(58 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since our models are a weighted average of multiple lobes, we randomly select one based on the energy and importance sample the visible normals [Heitz and d'Eon 2014] based on the fake incident direction and roughness. However, this strategy is not optimal and creates re ies since the di erent lobes overlap.…”

Section: Forward Modelmentioning

confidence: 99%

Efficient rendering of layered materials using an atomic decomposition with statistical operators

Belcour¹

2018

ACM Trans. Graph.

View full text Add to dashboard Cite

Fig. 1. We introduce a BSDF model to render plane-parallel layered materials using an analysis of the directional statistics of light interaction with microfacet geometry and participating media. Our model closely matches the reference and supports an arbitrary number of textured layers while being energy conserving, free from heavy per-material precomputation, and compatible with real-time constraints.We derive a novel framework for the e cient analysis and computation of light transport within layered materials. Our derivation consists of two steps. First, we decompose light transport into a set of atomic operators that act on its directional statistics. Speci cally, our operators consist of re ection, refraction, sca ering, and absorption, whose combinations are su cient to describe the statistics of light sca ering multiple times within layered structures. We show that the rst three directional moments (energy, mean and variance) already provide an accurate summary. Second, we extend the adding-doubling method to support arbitrary combinations of such operators e ciently. During shading, we map the directional moments to BSDF lobes. We validate that the resulting BSDF closely matches the ground truth in a lightweight and e cient form. Unlike previous methods, we support an arbitrary number of textured layers, and demonstrate a practical and accurate rendering of layered materials with both an o ine and real-time implementation that are free from per-material precomputation.

show abstract

Section: Forward Modelmentioning

confidence: 99%

Efficient rendering of layered materials using an atomic decomposition with statistical operators

Belcour¹

2018

ACM Trans. Graph.

View full text Add to dashboard Cite

show abstract

“…An appropriate sampler for microfacet models was introduced by Heitz and d'Eon [Hd14]. Thus, in most cases the variance in the rendered images does not originate in this part of the estimator.…”

Section: Variance With and Without Regularizationmentioning

confidence: 99%

Microfacet Model Regularization for Robust Light Transport

Jendersie

Grosch

2019

Computer Graphics Forum

View full text Add to dashboard Cite

Today, Monte Carlo light transport algorithms are used in many applications to render realistic images. Depending on the complexity of the used methods, several light effects can or cannot be found by the sampling process. Especially, specular and smooth glossy surfaces often lead to high noise and missing light effects. Path space regularization provides a solution, improving any sampling algorithm, by modifying the material evaluation code. Previously, Kaplanyan and Dachsbacher [KD13] introduced the concept for pure specular interactions. We extend this idea to the commonly used microfacet models by manipulating the roughness parameter prior to the evaluation. We also show that this kind of regularization requires a change in the MIS weight computation and provide the solution. Finally, we propose two heuristics to adaptively reduce the introduced bias. Using our method, many complex light effects are reproduced and the fidelity of smooth objects is increased. Additionally, if a path was sampleable before, the variance is partially reduced.

show abstract

“…In Section 5.4 we present a practical evaluation procedure. Heitz and d'Eon [2014] showed that microfacet BRDF models can be efficiently importance sampled by first sampling a normal from the distribution of visible normals Dω i , and then sampling the micro-BRDF of the material aligned with this normal. The same idea can be applied to importance sampling microflake phase functions: if we importance sample the VNDF Dω i to generate a sample ωm, and then sample an outgoing direction ωo with the micro-phase function p(ωm, ωi → ωo), then ωo follows the PDF given by the dot product of Dω i (ωm) and p(ωm, ωi → ωo).…”

Section: Diffuse Phase Functionmentioning

confidence: 99%

The SGGX microflake distribution

Heitz

Dupuy

Crassin³

et al. 2015

ACM Trans. Graph.

View full text Add to dashboard Cite

Figure 1: Top-left: rendering a voxelized forest at decreasing levels of detail (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. AbstractWe 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.

show abstract

Importance Sampling Microfacet‐Based BSDFs using the Distribution of Visible Normals

Cited by 71 publications

References 12 publications

Efficient rendering of layered materials using an atomic decomposition with statistical operators

Efficient rendering of layered materials using an atomic decomposition with statistical operators

Microfacet Model Regularization for Robust Light Transport

The SGGX microflake distribution

Contact Info

Product

Resources

About