1 About
1.1 Authors
2 Overview
2.1 Principles
2.2 Physically based rendering
3 Notation
4 Material system
4.1 Standard model
4.2 Dielectrics and conductors
4.3 Energy conservation
4.4 Specular BRDF
4.4.1 Normal distribution function (specular D)
4.4.2 Geometric shadowing (specular G)
4.4.3 Fresnel (specular F)
4.5 Diffuse BRDF
4.6 Standard model summary
4.7 Improving the BRDFs
4.7.1 Energy gain in diffuse reflectance
4.7.2 Energy loss in specular reflectance
4.8 Parameterization
4.8.1 Standard parameters
4.8.2 Types and ranges
4.8.3 Remapping
4.8.4 Blending and layering
4.8.5 Crafting physically based materials
4.9 Clear coat model
4.9.1 Clear coat specular BRDF
4.9.2 Integration in the surface response
4.9.3 Clear coat parameterization
4.9.4 Base layer modification
4.10 Anisotropic model
4.10.1 Anisotropic specular BRDF
4.10.2 Anisotropic parameterization
4.11 Subsurface model
4.11.1 Subsurface specular BRDF
4.11.2 Subsurface parameterization
4.12 Cloth model
4.12.1 Cloth specular BRDF
4.12.2 Cloth diffuse BRDF
4.12.3 Cloth parameterization
5 Lighting
5.1 Units
5.1.1 Light units validation
5.2 Direct lighting
5.2.1 Directional lights
5.2.2 Punctual lights
5.2.3 Photometric lights
5.2.4 Area lights
5.2.5 Lights parameterization
5.2.6 Preexposed lights
5.3 Image based lights
5.3.1 IBL Types
5.3.2 IBL Unit
5.3.3 Processing light probes
5.3.4 Distant light probes
5.3.5 Clear coat
5.3.6 Anisotropy
5.3.7 Subsurface
5.3.8 Cloth
5.4 Static lighting
5.5 Transparency and translucency lighting
5.5.1 Transparency
5.5.2 Translucency
5.6 Occlusion
5.6.1 Diffuse occlusion
5.6.2 Specular occlusion
5.7 Normal mapping
5.7.1 Reoriented normal mapping
5.7.2 UDN blending
6 Volumetric effects
6.1 Exponential height fog
7 Antialiasing
8 Imaging pipeline
8.1 Physically based camera
8.1.1 Exposure settings
8.1.2 Exposure value
8.1.3 Exposure
8.1.4 Automatic exposure
8.1.5 Bloom
8.2 Optics postprocessing
8.2.1 Color fringing
8.2.2 Lens flares
8.3 Filmic postprocessing
8.3.1 Contrast
8.3.2 Curves
8.3.3 Levels
8.3.4 Color grading
8.4 Light path
8.4.1 Clustered Forward Rendering
8.4.2 Implementation notes
8.5 Validation
8.5.1 Scene referred visualization
8.5.2 Reference renderings
8.6 Coordinates systems
8.6.1 World coordinates system
8.6.2 Camera coordinates system
8.6.3 Cubemaps coordinates system
9 Annex
9.1 Specular color
9.2 Importance sampling for the IBL
9.2.1 Choosing important directions
9.2.2 Prefiltered importance sampling
9.3 Choosing important directions for sampling the BRDF
9.4 Hammersley sequence
9.5 Precomputing L for imagebased lighting
9.6 Spherical Harmonics
9.6.1 Basis functions
9.6.2 Decomposition and reconstruction
9.6.3 Decomposition of \(\left< cos \theta \right>\)
9.6.4 Convolution
9.7 Sample validation scene for Mitsuba
9.8 Light assignment with froxels
10 Revisions
11 Bibliography
This document is part of the Filament project. To report errors in this document please use the project's issue tracker.
Filament is a physically based rendering (PBR) engine for Android. The goal of Filament is to offer a set of tools and APIs for Android developers that will enable them to create high quality 2D and 3D rendering with ease.
The goal of this document is to explain the equations and theory behind the material and lighting models used in Filament. This document is intended as a reference for contributors to Filament or developers interested in the inner workings of the engine. We will provide code snippets as needed to make the relationship between theory and practice as clear as possible.
This document is not intended as a design document. It focuses solely on algorithms and its content could be used to implement PBR in any engine. However, this document explains why we chose specific algorithms/models over others.
Unless noted otherwise, all the 3D renderings present in this document have been generated inengine (prototype or production). Many of these 3D renderings were captured during the early stages of development of Filament and do not reflect the final quality.
Realtime rendering is an active area of research and there is a large number of equations, algorithms and implementation to choose from for every single feature that needs to be implemented (the book Rendering realtime shadows, for instance, is a 400 pages summary of dozens of shadows rendering techniques). As such, we must first define our goals (or principles, to follow Brent Burley's seminal paper Physicallybased shading at Disney [Burley12]) before we can make informed decisions.
Our primary goal is to design and implement a rendering system able to perform efficiently on mobile platforms. The primary target will be OpenGL ES 3.x class GPUs.
Our rendering system will emphasize overall picture quality. We will however accept quality compromises to support low and medium performance GPUs.
Artists need to be able to iterate often and quickly on their assets and our rendering system must allow them to do so intuitively. We must therefore provide parameters that are easy to understand (for instance, no specular power, no index of refraction…).
We also understand that not all developers have the luxury to work with artists. The physically based approach of our system will allow developers to craft visually plausible materials without the need to understand the theory behind our implementation.
For both artists and developers, our system will rely on as few parameters as possible to reduce trial and error and allow users to quickly master the material model.
In addition, any combination of parameter values should lead to physically plausible results. Physically implausible materials must be hard to create.
Our system should use physical units everywhere possible: distances in meters or centimeters, color temperatures in Kelvin, light units in lumens or candelas, etc.
A physically based approach must not preclude nonrealistic rendering. User interfaces for instance will need unlit materials.
While not directly related to the content of this document, it bears emphasizing our desire to keep the rendering library as small as possible so any application can bundle it without increasing the binary to undesirable sizes.
We chose to adopt PBR for its benefits from an artistic and production efficient standpoints, and because it is compatible with our goals.
Physically based rendering is a rendering method that provides a more accurate representation of materials and how they interact with light when compared to traditional realtime models. The separation of materials and lighting at the core of the PBR method makes it easier to create realistic assets that look accurate in all lighting conditions.
$$ \newcommand{NoL}{n \cdot l} \newcommand{NoV}{n \cdot v} \newcommand{NoH}{n \cdot h} \newcommand{VoH}{v \cdot h} \newcommand{LoH}{l \cdot h} \newcommand{fNormal}{f_{0}} \newcommand{fDiffuse}{f_d} \newcommand{fSpecular}{f_r} \newcommand{fX}{f_x} \newcommand{aa}{\alpha^2} \newcommand{fGrazing}{f_{90}} \newcommand{schlick}{F_{Schlick}} \newcommand{nior}{n_{ior}} \newcommand{Ed}{E_d} \newcommand{Lt}{L_{\bot}} \newcommand{Lout}{L_{out}} \newcommand{cosTheta}{\left< \cos \theta \right> } $$
The equations found throughout this document use the symbols described in table 1.
Symbol  Definition 

\(v\)  View unit vector 
\(l\)  Incident light unit vector 
\(n\)  Surface normal unit vector 
\(h\)  Half unit vector between \(l\) and \(v\) 
\(f\)  BRDF 
\(\fDiffuse\)  Diffuse component of a BRDF 
\(\fSpecular\)  Specular component of a BRDF 
\(\alpha\)  Roughness, remapped from using input perceptualRoughness 
\(\sigma\)  Diffuse reflectance 
\(\Omega\)  Spherical domain 
\(\fNormal\)  Reflectance at normal incidence 
\(\fGrazing\)  Reflectance at grazing angle 
\(\chi^+(a)\)  Heaviside function (1 if \(a > 0\) and 0 otherwise) 
\(n_{ior}\)  Index of refraction (IOR) of an interface 
\(\left< \NoL \right>\)  Dot product clamped to [0..1] 
\(\left< a \right>\)  Saturated value (clamped to [0..1]) 
The sections below describe multiple material models to simplify the description of various surface features such as anisotropy or the clear coat layer. In practice however some of these models are condensed into a single one. For instance, the standard model, the clear coat model and the anisotropic model can be combined to form a single, more flexible and powerful model. Please refer to the Materials documentation to get a description of the material models as implemented in Filament.
The goal of our model is to represent standard material appearances. A material model is described mathematically by a BSDF (Bidirectional Scattering Distribution Function), which is itself composed of two other functions: the BRDF (Bidirectional Reflectance Distribution Function) and the BTDF (Bidirectional Transmittance Function).
Since we aim to model commonly encountered surfaces, our standard material model will focus on the BRDF and ignore the BTDF, or approximate it greatly. Our standard model will therefore only be able to correctly mimic reflective, isotropic, dielectric or conductive surfaces with short mean free paths.
The BRDF describes the surface response of a standard material as a function made of two terms:
The relationship between a surface, the surface normal, incident light and these terms is shown in figure 1 (we ignore subsurface scattering for now):
The complete surface response can be expressed as such:
$$\begin{equation}\label{brdf} f(v,l)=f_d(v,l)+f_r(v,l) \end{equation}$$
This equation characterizes the surface response for incident light from a single direction. The full rendering equation would require to integrate \(l\) over the entire hemisphere.
Commonly encountered surfaces are usually not made of a flat interface so we need a model that can characterize the interaction of light with an irregular interface.
A microfacet BRDF is a good physically plausible BRDF for that purpose. Such BRDF states that surfaces are not smooth at a micro level, but made of a large number of randomly aligned planar surface fragments, called microfacets. Figure 2 shows the difference between a flat interface and an irregular interface at a micro level:
Only the microfacets whose normal is oriented halfway between the light direction and the view direction will reflect visible light, as shown in figure 3.
However, not all microfacets with a properly oriented normal will contribute reflected light as the BRDF takes into account masking and shadowing. This is illustrated in figure 4.
A microfacet BRDF is heavily influenced by a roughness parameter which describes how smooth (low roughness) or how rough (high roughness) a surface is at a micro level. The smoother the surface, the more facets are aligned and the more pronounced the reflected light is. The rougher the surface, the fewer facets are oriented towards the camera and incoming light is scattered away from the camera after reflection, giving a blurry aspect to the specular highlights.
Figure 5 shows surfaces of different roughness and how light interacts with them.
The roughness parameter as set by the user is called perceptualRoughness
in the shader snippets throughout this document. The variable called roughness
is the perceptualRoughness
with a remapping explained in section 4.8.
A microfacet model is described by the following equation (where x stands for the specular or diffuse component):
$$\begin{equation} \fX(v,l) = \frac{1}{ \NoV   \NoL } \int_\Omega D(m,\alpha) G(v,l,m) f_m(v,l,m) (v \cdot m) (l \cdot m) dm \end{equation}$$
The term \(D\) models the distribution of the microfacets (this term is also referred to as the NDF or Normal Distribution Function). This term plays a primordial role in the appearance of surfaces as shown in figure 5.
The term \(G\) models the visibility (or occlusion or shadowmasking) of the microfacets.
Since this equation is valid for both the specular and diffuse components, the difference lies in the microfacet BRDF \(f_m\).
It is important to note that this equation is used to integrate over the hemisphere at a micro level:
The diagram above shows that at a macro level, the surfaces is considered flat. This helps simplify our equations by assuming that a shaded fragment lit from a single direction corresponds to a single point at the surface.
At a micro level however, the surface is not flat and we cannot assume a single ray of light anymore (we can however assume that the incident rays are parallel). Since the micro facets will scatter the light in different directions given a bundle of parallel incident rays, we must integrate the surface response over a hemisphere, noted m in the above diagram.
It is obviously not practical to compute the full integration over the microfacets hemisphere for each shaded fragment. We will therefore rely on approximations of the integration for both the specular and diffuse components.
To better understand some of the equations and behaviors shown below, we must first clearly understand the difference between metallic (conductor) and nonmetallic (dielectric) surfaces.
We saw earlier that when incident light hits a surface governed by a BRDF, the light is reflected as two separate components: the diffuse reflectance and the specular reflectance. The modelization of this behavior is straightforward as shown in figure 7.
This modelization is a simplification of how the light actually interacts with the surface. In reality, part of the incident light will penetrate the surface, scatter inside, and exit the surface again as diffuse reflectance. This phenomenon is illustrated in figure 8.
Here lies the difference between conductors and dielectrics. There is no subsurface scattering occurring with purely metallic materials, which means there is no diffuse component (and we will see later that this has an influence on the perceived color of the specular component). Scattering happens in dielectrics, which means they have both specular and diffuse components.
To properly modelize the BRDF we must therefore distinguish between dielectrics and conductors (scattering not shown for clarity), as shown in figure 9.
Energy conservation is one of the key components of a good BRDF for physically based rendering. An energy conservative BRDF states that the total amount of specular and diffuse reflectance energy is less than the total amount of incident energy. Without an energy conservative BRDF, artists must manually ensure that the light reflected off a surface is never more intense than the incident light.
For the specular term, \(f_m\) is a mirror BRDF that can be modeled with the Fresnel law, noted \(F\) in the CookTorrance approximation of the microfacet model integration:
$$\begin{equation} f_r(v,l) = \frac{D(h, \alpha) G(v, l, \alpha) F(v, h, f0)}{4(\NoV)(\NoL)} \end{equation}$$
Given our realtime constraints, we must use an approximation for the three terms \(D\), \(G\) and \(F\). [Karis13] has compiled a great list of formulations for these three terms that can be used with the CookTorrance specular BRDF. The sections that follow describe the equations we picked for these terms.
[Burley12] observed that longtailed normal distribution functions (NDF) are a good fit for realworld surfaces. The GGX distribution described in [Walter07] is a distribution with longtailed falloff and short peak in the highlights, with a simple formulation suitable for realtime implementations. It is also a popular model, equivalent to the TrowbridgeReitz distribution, in modern physically based renderers.
$$\begin{equation} D_{GGX}(h,\alpha) = \frac{\aa}{\pi ( (\NoH)^2 (\aa  1) + 1)^2} \end{equation}$$
The GLSL implementation of the NDF, shown in listing 1, is simple and efficient.
float D_GGX(float NoH, float roughness) {
float a = NoH * roughness;
float k = roughness / (1.0  NoH * NoH + a * a);
return k * k * (1.0 / PI);
}
We can improve this implementation by using half precision floats. This optimization requires changes to the original equation as there are two problems when computing \(1  (\NoH)^2\) in halffloats. First, this computation suffers from floating point cancellation when \((\NoH)^2\) is close to 1 (highlights). Secondly \(\NoH\) does not have enough precision around 1.
The solution involves Lagrange's identity:
$$\begin{equation}  a \times b ^2 = a^2 b^2  (a \cdot b)^2 \end{equation}$$
Since both \(n\) and \(h\) are unit vectors, \(n \times h^2 = 1  (\NoH)^2\). This allows us to compute \(1  (\NoH)^2\) directly with half precision floats by using a simple cross product. Listing 2 shows the final optimized implementation.
#define MEDIUMP_FLT_MAX 65504.0
#define saturateMediump(x) min(x, MEDIUMP_FLT_MAX)
float D_GGX(float roughness, float NoH, const vec3 n, const vec3 h) {
vec3 NxH = cross(n, h);
float a = NoH * roughness;
float k = roughness / (dot(NxH, NxH) + a * a);
float d = k * k * (1.0 / PI);
return saturateMediump(d);
}
Eric Heitz showed in [Heitz14] that the Smith geometric shadowing function is the correct and exact \(G\) term to use. The Smith formulation is the following:
$$\begin{equation} G(v,l,\alpha) = G_1(l,\alpha) G_1(v,\alpha) \end{equation}$$
\(G_1\) can in turn follow several models, and is commonly set to the GGX formulation:
$$\begin{equation} G_1(v,\alpha) = G_{GGX}(v,\alpha) = \frac{2 (\NoV)}{\NoV + \sqrt{\aa + (1  \aa) (\NoV)^2}} \end{equation}$$
The full SmithGGX formulation thus becomes:
$$\begin{equation} G(v,l,\alpha) = \frac{2 (\NoL)}{\NoL + \sqrt{\aa + (1  \aa) (\NoL)^2}} \frac{2 (\NoV)}{\NoV + \sqrt{\aa + (1  \aa) (\NoV)^2}} \end{equation}$$
We can observe that the dividends \(2 (\NoL)\) and \(2 (n \cdot v)\) allow us to simplify the original function \(f_r\) by introducing a visibility function \(V\):
$$\begin{equation} f_r(v,l) = D(h, \alpha) V(v, l, \alpha) F(v, h, f_0) \end{equation}$$
Where:
$$\begin{equation} V(v,l,\alpha) = \frac{G(v, l, \alpha)}{4 (\NoV) (\NoL)} = V_1(l,\alpha) V_1(v,\alpha) \end{equation}$$
And:
$$\begin{equation} V_1(v,\alpha) = \frac{1}{\NoV + \sqrt{\aa + (1  \aa) (\NoV)^2}} \end{equation}$$
Heitz notes however that taking the height of the microfacets into account to correlate masking and shadowing leads to more accurate results. He defines the heightcorrelated Smith function thusly:
$$\begin{equation} G(v,l,h,\alpha) = \frac{\chi^+(\VoH) \chi^+(\LoH)}{1 + \Lambda(v) + \Lambda(l)} \end{equation}$$
$$\begin{equation} \Lambda(m) = \frac{1 + \sqrt{1 + \aa tan^2(\theta_m)}}{2} = \frac{1 + \sqrt{1 + \aa \frac{(1  cos^2(\theta_m))}{cos^2(\theta_m)}}}{2} \end{equation}$$
Replacing \(\theta_m\) by \(\NoV\), we obtain:
$$\begin{equation} \Lambda(v) = \frac{1}{2} \left( \frac{\sqrt{\aa + (1  \aa)(\NoV)^2}}{\NoV}  1 \right) \end{equation}$$
From which we can derive the visibility function:
$$\begin{equation} V(v,l,\alpha) = \frac{0.5}{\NoL \sqrt{(\NoV)^2 (1  \aa) + \aa} + \NoV \sqrt{(\NoL)^2 (1  \aa) + \aa}} \end{equation}$$
The GLSL implementation of the visibility term, shown in listing 3, is a bit more expensive than we would like since it requires two sqrt
operations.
float V_SmithGGXCorrelated(float NoV, float NoL, float roughness) {
float a2 = roughness * roughness;
float GGXV = NoL * sqrt(NoV * NoV * (1.0  a2) + a2);
float GGXL = NoV * sqrt(NoL * NoL * (1.0  a2) + a2);
return 0.5 / (GGXV + GGXL);
}
We can optimize this visibility function by using an approximation after noticing that all the terms under the square roots are squares and that all the terms are in the \([0..1]\) range:
$$\begin{equation} V(v,l,\alpha) = \frac{0.5}{\NoL (\NoV (1  \alpha) + \alpha) + \NoV (\NoL (1  \alpha) + \alpha)} \end{equation}$$
This approximation is mathematically wrong but saves two square root operations and is good enough for realtime mobile applications, as shown in listing 4.
float V_SmithGGXCorrelatedFast(float NoV, float NoL, float roughness) {
float a = roughness;
float GGXV = NoL * (NoV * (1.0  a) + a);
float GGXL = NoV * (NoL * (1.0  a) + a);
return 0.5 / (GGXV + GGXL);
}
[Hammon17] proposes the same approximation based on the same observation that the square root can be removed. It does so by rewriting the expressions as lerps:
$$\begin{equation} V(v,l,\alpha) = \frac{0.5}{lerp(2 (\NoL) (\NoV), \NoL + \NoV, \alpha)} \end{equation}$$
The Fresnel effect plays an important role in the appearance of physically based materials. This effect models the fact that the amount of light the viewer sees reflected from a surface depends on the viewing angle. Large bodies of water are a perfect way to experience this phenomenon, as shown in figure 10. When looking at the water straight down (at normal incidence) you can see through the water. However, when looking further out in the distance (at grazing angle, where perceived light rays are getting parallel to the surface), you will see the specular reflections on the water become more intense.
The amount of light reflected depends not only on the viewing angle, but also on the index of refraction (IOR) of the material. At normal incidence (perpendicular to the surface, or 0° angle), the amount of light reflected back is noted \(\fNormal\) and can be derived from the IOR as we will see in section 4.8.3.2. The amount of light reflected back at grazing angle is noted \(\fGrazing\) and approaches 100% for smooth materials.
More formally, the Fresnel term defines how light reflects and refracts at the interface between two different media, or the ratio of reflected and transmitted energy. [Schlick94] describes an inexpensive approximation of the Fresnel term for the CookTorrance specular BRDF:
$$\begin{equation} F_{Schlick}(v,h,\fNormal,\fGrazing) = \fNormal + (\fGrazing  \fNormal)(1  \VoH)^5 \end{equation}$$
The constant \(\fNormal\) represents the specular reflectance at normal incidence and is achromatic for dielectrics, and chromatic for metals. The actual value depends on the index of refraction of the interface. The GLSL implementation of this term requires the use of a pow
, as shown in listing 5, which can be replaced by a few multiplications.
vec3 F_Schlick(float VoH, vec3 f0, float f90) {
return f0 + (vec3(f90)  f0) * pow(1.0  VoH, 5.0);
}
This Fresnel function can be seen as interpolating between the incident specular reflectance and the reflectance at grazing angles, represented here by \(\fGrazing\). Observation of real world materials show that both dielectrics and conductors exhibit achromatic specular reflectance at grazing angles and that the Fresnel reflectance is 1.0 at 90°. A more correct \(\fGrazing\) is discussed in section 5.6.2.
Using \(\fGrazing\) set to 1, the Schlick approximation for the Fresnel term can be optimized for scalar operations by refactoring the code slightly. The result is shown in listing 6.
vec3 F_Schlick(float VoH, vec3 f0) {
float f = pow(1.0  VoH, 5.0);
return f + f0 * (1.0  f);
}
In the diffuse term, \(f_m\) is a Lambertian function and the diffuse term of the BRDF becomes:
$$\begin{equation} \fDiffuse(v,l) = \frac{\sigma}{\pi} \frac{1}{ \NoV   \NoL } \int_\Omega D(m,\alpha) G(v,l,m) (v \cdot m) (l \cdot m) dm \end{equation}$$
Our implementation will instead use a simple Lambertian BRDF that assumes a uniform diffuse response over the microfacets hemisphere:
$$\begin{equation} \fDiffuse(v,l) = \frac{\sigma}{\pi} \end{equation}$$
In practice, the diffuse reflectance \(\sigma\) is multiplied later, as shown in listing 8.
float Fd_Lambert() {
return 1.0 / PI;
}
vec3 Fd = diffuseColor * Fd_Lambert();
The Lambertian BRDF is obviously extremely efficient and delivers results close enough to more complex models.
However, the diffuse part would ideally be coherent with the specular term and take into account the surface roughness. Both the Disney diffuse BRDF [Burley12] and OrenNayar model [Oren94] take the roughness into account and create some retroreflection at grazing angles. Given our constraints we decided that the extra runtime cost does not justify the slight increase in quality. This sophisticated diffuse model also renders imagebased and spherical harmonics more difficult to express and implement.
For completeness, the Disney diffuse BRDF expressed in [Burley12] is the following:
$$\begin{equation} \fDiffuse(v,l) = \frac{\sigma}{\pi} \schlick(n,l,1,\fGrazing) \schlick(n,v,1,\fGrazing) \end{equation}$$
Where:
$$\begin{equation} \fGrazing=0.5 + 2 \cdot \alpha cos^2(\theta_d) \end{equation}$$
float F_Schlick(float VoH, float f0, float f90) {
return f0 + (f90  f0) * pow(1.0  VoH, 5.0);
}
float Fd_Burley(float NoV, float NoL, float LoH, float roughness) {
float f90 = 0.5 + 2.0 * roughness * LoH * LoH;
float lightScatter = F_Schlick(NoL, 1.0, f90);
float viewScatter = F_Schlick(NoV, 1.0, f90);
return lightScatter * viewScatter * (1.0 / PI);
}
Figure 11 shows a comparison between a simple Lambertian diffuse BRDF and the higher quality Disney diffuse BRDF, using a fully rough dielectric material. For comparison purposes, the right sphere was mirrored. The surface response is very similar with both BRDFs but the Disney one exhibits some nice retroreflections at grazing angles (look closely at the left edge of the spheres).
We could allow artists/developers to choose the Disney diffuse BRDF depending on the quality they desire and the performance of the target device. It is important to note however that the Disney diffuse BRDF is not energy conserving as expressed here.
Specular term: a CookTorrance specular microfacet model, with a GGX normal distribution function, a SmithGGX heightcorrelated visibility function, and a Schlick Fresnel function.
Diffuse term: a Lambertian diffuse model.
The full GLSL implementation of the standard model is shown in listing 9.
float D_GGX(float NoH, float a) {
float a2 = a * a;
float f = (NoH * a2  NoH) * NoH + 1.0;
return a2 / (PI * f * f);
}
vec3 F_Schlick(float VoH, vec3 f0) {
return f0 + (vec3(1.0)  f0) * pow(1.0  VoH, 5.0);
}
float V_SmithGGXCorrelated(float NoV, float NoL, float a) {
float a2 = a * a;
float GGXL = NoV * sqrt((NoL * a2 + NoL) * NoL + a2);
float GGXV = NoL * sqrt((NoV * a2 + NoV) * NoV + a2);
return 0.5 / (GGXV + GGXL);
}
float Fd_Lambert() {
return 1.0 / PI;
}
void BRDF(...) {
vec3 h = normalize(v + l);
float NoV = abs(dot(n, v)) + 1e5;
float NoL = clamp(dot(n, l), 0.0, 1.0);
float NoH = clamp(dot(n, h), 0.0, 1.0);
float LoH = clamp(dot(l, h), 0.0, 1.0);
// perceptually linear roughness to roughness (see parameterization)
float roughness = perceptualRoughness * perceptualRoughness;
float D = D_GGX(NoH, a);
vec3 F = F_Schlick(LoH, f0);
float V = V_SmithGGXCorrelated(NoV, NoL, roughness);
// specular BRDF
vec3 Fr = (D * V) * F;
// diffuse BRDF
vec3 Fd = diffuseColor * Fd_Lambert();
// apply lighting...
}
We mentioned in section 4.3 that energy conservation is one of the key components of a good BRDF. Unfortunately the BRDFs explored previously suffer from two problems that we will examine below.
The Lambert diffuse BRDF does not account for the light that reflects at the surface and that is therefore not able to participate in the diffuse scattering event.
[TODO: talk about the issue with fr+fd]
The CookTorrance BRDF we presented earlier attempts to model several events at the microfacet level but does so by accounting for a single bounce of light. This approximation can cause a loss of energy at high roughness, the surface is not energy preserving. Figure 12 shows why this loss of energy occurs. In the single bounce (or single scattering) model, a ray of light hitting the surface can be reflected back onto another microfacet and thus be discarded because of the masking and shadowing term. If we however account for multiple bounces (multiscattering), the same ray of light might escape the microfacet field and be reflected back towards the viewer.
Based on this simple explanation, we can intuitively deduce that the rougher a surface is, the higher the chances are that energy gets lost because of the failure to account for multiple scattering events. This loss of energy appears to darken rough materials. Metallic surfaces are particularly affected because all of their reflectance is specular. This darkening effect is illustrated in figure 13. With multiscattering, energy preservation can be achieved, as shown in figure 14.
We can use a white furnace, a uniform lighting environment set to pure white, to validate the energy preservation property of a BRDF. When energy preservation is achieved, a purely reflective metallic surface (\(\fNormal = 1\)) should be indistinguishable from the background, no matter the roughness of said surface. Figure 15 shows what such a surface looks like with the specular BRDF presented in the previous sections. The loss of energy as the roughness increases is obvious. In contrast, figure 16 shows that accounting for multiscattering events addresses the energy loss.
Multiplescattering microfacet BRDFs are discussed in depth in [Heitz16]. Unfortunately this paper only presents a stochastic evaluation of the multiscattering BRDF. This solution is therefore not suitable for realtime rendering. Kulla and Conty present a different approach in [Kulla17]. Their idea is to add an energy compensation term as an additional BRDF lobe shown in equation \(\ref{energyCompensationLobe}\):
$$\begin{equation}\label{energyCompensationLobe} f_{ms}(l,v) = \frac{(1  E(l)) (1  E(v)) F_{avg}^2 E_{avg}}{\pi (1  E_{avg}) (1  F_{avg}(1  E_{avg}))} \end{equation}$$
Where \(E\) is the directional albedo of the specular BRDF \(f_r\), with \(\fNormal\) set to 1:
$$\begin{equation} E(l) = \int_{\Omega} f(l,v) (\NoV) dv \end{equation}$$
The term \(E_{avg}\) is the cosineweighted average of \(E\):
$$\begin{equation} E_{avg} = 2 \int_0^1 E(\mu) \mu d\mu \end{equation}$$
Similarly, \(F_{avg}\) is the cosineweighted average of the Fresnel term:
$$\begin{equation} F_{avg} = 2 \int_0^1 F(\mu) \mu d\mu \end{equation}$$
Both terms \(E\) and \(E_{avg}\) can be precomputed and stored in lookup tables. while \(F_{avg}\) can be greatly simplified when the Schlick approximation is used:
$$\begin{equation}\label{averageFresnel} F_{avg} = \frac{1 + 20 \fNormal}{21} \end{equation}$$
This new lobe is combined with the original single scattering lobe, previously noted \(f_r\):
$$\begin{equation} f_{r}(l,v) = f_{ss}(l,v) + f_{ms}(l,v) \end{equation}$$
In [Lagarde18], with credit to Emmanuel Turquin, Lagarde and Golubev make the observation that equation \(\ref{averageFresnel}\) can be simplified to \(\fNormal\). They also propose to apply energy compensation by adding a scaled GGX specular lobe:
$$\begin{equation}\label{energyCompensation} f_{ms}(l,v) = \fNormal \frac{1  E(l)}{E(l)} f_{ss}(l,v) \end{equation}$$
The key insight is that \(E(l)\) can not only be precomputed but also shared with imagebased lighting preintegration. The multiscattering energy compensation formula thus becomes:
$$\begin{equation}\label{scaledEnergyCompensationLobe} f_r(l,v) = f_{ss}(l,v) + \fNormal \left( \frac{1}{r}  1 \right) f_{ss}(l,v) \end{equation}$$
Where \(r\) is defined as:
$$\begin{equation} r = \int_{\Omega} D(l,v) V(l,v) \left< \NoL \right> dl \end{equation}$$
We can implement specular energy compensation at a negligible cost if we store \(r\) in the DFG lookup table presented in section 5.3. Listing 10 shows that the implementation is a direct conversion of equation \(\ref{scaledEnergyCompensationLobe}\).
vec3 energyCompensation = 1.0 + f0 * (1.0 / dfg.y  1.0);
// Scale the specular lobe to account for multiscattering
Fr *= pixel.energyCompensation;
Please refer to section 5.3 and section 5.3.4.7 to learn how the DFG lookup table is derived and computed.
Disney's material model described in [Burley12] is a good starting point but its numerous parameters makes it impractical for realtime implementations. In addition, we would like our standard material model to be easy to understand and easy to use for both artists and developers.
Table 2 describes the list of parameters that satisfy our constraints.
Parameter  Definition 

BaseColor  Diffuse albedo for nonmetallic surfaces, and specular color for metallic surfaces 
Metallic  Whether a surface appears to be dielectric (0.0) or conductor (1.0). Often used as a binary value (0 or 1) 
Roughness  Perceived smoothness (0.0) or roughness (1.0) of a surface. Smooth surfaces exhibit sharp reflections 
Reflectance  Fresnel reflectance at normal incidence for dielectric surfaces. This replaces an explicit index of refraction 
Emissive  Additional diffuse albedo to simulate emissive surfaces (such as neons, etc.) This parameter is mostly useful in an HDR pipeline with a bloom pass 
Ambient occlusion  Defines how much of the ambient light is accessible to a surface point. It is a perpixel shadowing factor between 0.0 and 1.0. This parameter will be discussed in more details in the lighting section 
Figure 17 shows how the metallic, roughness and reflectance parameters affect the appearance of a surface.
It is important to understand the type and range of the different parameters of our material model, described in table 3.
Parameter  Type and range 

BaseColor  Linear RGB [0..1] 
Metallic  Scalar [0..1] 
Roughness  Scalar [0..1] 
Reflectance  Scalar [0..1] 
Emissive  Linear RGB [0..1] + exposure compensation 
Ambient occlusion  Scalar [0..1] 
Note that the types and ranges described here are what the shader will expect. The API and/or tools UI could and should allow to specify the parameters using other types and ranges when they are more intuitive for artists.
For instance, the base color could be expressed in sRGB space and converted to linear space before being sent off to the shader. It can also be useful for artists to express the metallic, roughness and reflectance parameters as gray values between 0 and 255 (black to white).
Another example: the emissive parameter could be expressed as a color temperature and an intensity, to simulate the light emitted by a black body.
To make the standard material model easier and more intuitive to use for artists, we must remap the parameters baseColor, roughness and reflectance.
The base color of a material is affected by the “metallicness” of said material. Dielectrics have achromatic specular reflectance but retain their base color as the diffuse color. Conductors on the other hand use their base color as the specular color and do not have a diffuse component.
The lighting equations must therefore use the diffuse color and \(\fNormal\) instead of the base color. The diffuse color can easily be computed from the base color, as show in listing 11.
vec3 diffuseColor = (1.0  metallic) * baseColor.rgb;
Dielectrics
The Fresnel term relies on \(\fNormal\), the specular reflectance at normal incidence angle, and is achromatic for dielectrics. We will use the remapping for dielectric surfaces described in [Lagarde14] :
$$\begin{equation} \fNormal = 0.16 \cdot reflectance^2 \end{equation}$$
The goal is to map \(\fNormal\) onto a range that can represent the Fresnel values of both common dielectric surfaces (4% reflectance) and gemstones (8% to 16%). The mapping function is chosen to yield a 4% Fresnel reflectance value for an input reflectance of 0.5 (or 128 on a linear RGB gray scale). Figure 18 show those common values and how they relate to the mapping function.
If the index of refraction is known (for instance, an airwater interface has an IOR of 1.33), the Fresnel reflectance can be calculated as follows:
$$\begin{equation}\label{fresnelEquation} \fNormal(n_{ior}) = \frac{(\nior  1)^2}{(\nior + 1)^2} \end{equation}$$
And if the reflectance value is known, we can compute the corresponding IOR:
$$\begin{equation} n_{ior} = \frac{2}{1  \sqrt{\fNormal}}  1 \end{equation}$$
Table 4 describes acceptable Fresnel reflectance values for various types of materials (no real world material has a value under 2%).
Material  Reflectance  Linear value 

Water  2%  0.35 
Fabric  4% to 5.6%  0.5 to 0.59 
Common liquids  2% to 4%  0.35 to 0.5 
Common gemstones  5% to 16%  0.56 to 1.0 
Plastics, glass  4% to 5%  0.5 to 0.56 
Other dielectric materials  2% to 5%  0.35 to 0.56 
Eyes  2.5%  0.39 
Skin  2.8%  0.42 
Hair  4.6%  0.54 
Teeth  5.8%  0.6 
Default value  4%  0.5 
Table 5 lists the \(\fNormal\) values for a few metals. The values are given in sRGB and must be used as the base color in our material model. Please refer to the annex, section 9.1, for an explanation of how these sRGB colors are computed from measured data.
Metal  \(\fNormal\) in sRGB  Hexadecimal  Color 

Silver  0.97, 0.96, 0.91  #f7f4e8  
Aluminum  0.91, 0.92, 0.92  #e8eaea  
Titanium  0.76, 0.73, 0.69  #c1baaf  
Iron  0.77, 0.78, 0.78  #c4c6c6  
Platinum  0.83, 0.81, 0.78  #d3cec6  
Gold  1.00, 0.85, 0.57  #ffd891  
Brass  0.98, 0.90, 0.59  #f9e596  
Copper  0.97, 0.74, 0.62  #f7bc9e  
All materials have a Fresnel reflectance of 100% at grazing angles so we will set \(\fGrazing\) in the following way when evaluating the specular BRDF \(\fSpecular\):
$$\begin{equation} \fGrazing = 1.0 \end{equation}$$
Figure 19 shows a red plastic ball. If you look closely at the edges of the sphere, you will be able to notice the achromatic specular reflectance at grazing angles.
Conductors
The specular reflectance of metallic surfaces is chromatic:
$$\begin{equation} \fNormal = baseColor \cdot metallic \end{equation}$$
Listing 12 shows how \(\fNormal\) is computed for both dielectric and metallic materials. It shows that the color of the specular reflectance is derived from the base color in the metallic case.
vec3 f0 = 0.16 * reflectance * reflectance * (1.0  metallic) + baseColor * metallic;
The roughness set by the user, called perceptualRoughness
here, is remapped to a perceptually linear range using the following formulation:
$$\begin{equation} \alpha = perceptualRoughness^2 \end{equation}$$
Figure 20 shows a silver metallic surface with increasing roughness (from 0.0 to 1.0), using the unmodified roughness value (bottom) and the remapped value (top).
Using this visual comparison, it is obvious that the remapped roughness is easier to understand by artists and developers. Without this remapping, shiny metallic surfaces would have to be confined to a very small range between 0.0 and 0.05.
Brent Burley made similar observations in his presentation [Burley12]. After experimenting with other remappings (cubic and quadratic mappings for instance), we have reached the conclusion that this simple square remapping delivers visually pleasing and intuitive results while being cheap for realtime applications.
Last but not least, it is important to note that the roughness parameters is used in various computations at runtime where limited floating point precision can become an issue. For instance, mediump precision floats are often implemented as halffloats (fp16) on mobile GPUs.
This cause problems when computing small values like \(\frac{1}{perceptualRoughness^4}\) in our lighting equations (roughness squared in the GGX computation). The smallest value that can be represented as a halffloat is \(2^{14}\) or \(6.1 \times 10^{5}\). To avoid divisions by 0 on devices that do not support denormals, the result of \(\frac{1}{roughness^4}\) must therefore not be lower than \(6.1 \times 10^{5}\). To do so, we must clamp the roughness to 0.089, which gives us \(6.274 \times 10^{5}\).
Denormals should also be avoided to prevent performance drops. The roughness can also not be set to 0 to avoid obvious divisions by 0.
Since we also want specular highlights to have a minimum size (a roughness close to 0 creates almost invisible highlights), we should clamp the roughness to a safe range in the shader. This clamping has the added benefit of correcting specular aliasing^{1} that can appear for low roughness values.
As noted in [Burley12] and [Neubelt13], this model allows for robust blending between different materials by simply interpolating the different parameters. In particular, this allows to layer different materials using simple masks.
For instance, figure 21 shows how the studio Ready at Dawn used material blending and layering in The Order: 1886 to create complex appearances from a library of simple materials (gold, copper, wood, rust, etc.).
The blending and layering of materials is effectively an interpolation of the various parameters of the material model. Figure 22 show an interpolation between shiny metallic chrome and rough red plastic. While the intermediate blended materials make little physical sense, they look plausible.
Designing physically based materials is fairly easy once you understand the nature of the four main parameters: base color, metallic, roughness and reflectance.
We provide a useful chart/reference guide to help artists and developers craft their own physically based materials.
In addition, here is a quick summary of how to use our material model:
Base color should be devoid of lighting information, except for microocclusion.
Metallic is almost a binary value. Pure conductors have a metallic value of 1 and pure dielectrics have a metallic value of 0. You should try to use values close at or close to 0 and 1. Intermediate values are meant for transitions between surface types (metal to rust for instance).
Base color represents the reflected color and should be an sRGB value in the range 50240 (strict range) or 30240 (tolerant range).
Metallic should be 0 or close to 0.
Reflectance should be set to 127 sRGB (0.5 linear, 4% reflectance) if you cannot find a proper value. Do not use values under 90 sRGB (0.35 linear, 2% reflectance).
Base color represents both the specular color and reflectance. Use values with a luminosity of 67% to 100% (170255 sRGB). Oxidized or dirty metals should use a lower luminosity than clean metals to take into account the nonmetallic components.
Metallic should be 1 or close to 1.
Reflectance is ignored (calculated from the base color).
The standard material model described previously is a good fit for isotropic surfaces made of a single layer. Multilayer materials are unfortunately fairly common, particularly materials with a thin translucent layer over a standard layer. Real world examples of such materials include car paints, soda cans, lacquered wood, acrylic, etc.
A clear coat layer can be simulated as an extension of the standard material model by adding a second specular lobe, which implies evaluating a second specular BRDF. To simplify the implementation and parameterization, the clear coat layer will always be isotropic and dielectric. The base layer can be anything allowed by the standard model (dielectric or conductor).
Since incoming light will traverse the clear coat layer, we must also take the loss of energy into account as shown in figure 24. Our model will however not simulate inter reflection and refraction behaviors.
The clear coat layer will be modeled using the same CookTorrance microfacet BRDF used in the standard model. Since the clear coat layer is always isotropic and dielectric, with low roughness values (see section 4.9.3), we can choose cheaper DFG terms without notably sacrificing visual quality.
A survey of the terms listed in [Karis13] and [Burley12] shows that the Fresnel and NDF terms we already use in the standard model are not computationally more expensive than other terms. [Kelemen01] describes a much simpler term that can replace our SmithGGX visibility term:
$$\begin{equation} V(l,h) = \frac{1}{4(\LoH)^2} \end{equation}$$
This maskingshadowing function is not physically based, as shown in [Heitz14], but its simplicity makes it desirable for realtime rendering.
In summary, our clear coat BRDF is a CookTorrance specular microfacet model, with a GGX normal distribution function, a Kelemen visibility function, and a Schlick Fresnel function. Listing 13 shows how trivial the GLSL implementation is.
float V_Kelemen(float LoH) {
return 0.25 / (LoH * LoH);
}
Note on the Fresnel term
The Fresnel term of the specular BRDF requires \(\fNormal\), the specular reflectance at normal incidence angle. This parameter can be computed from an index of refraction of an interface. We will assume that our clear coat layer is made of polyurethane, a common compound used in coatings and varnishes, or similar. An airpolyurethane interface has an IOR of 1.5, from which we can deduce \(\fNormal\):
$$\begin{equation} \fNormal(1.5) = \frac{(1.5  1)^2}{(1.5 + 1)^2} = 0.04 \end{equation}$$
This corresponds to a Fresnel reflectance of 4% that we know is associated with common dielectric materials.
Because we must take into account the loss of energy caused by the addition of the clear coat layer, we can reformulate the BRDF from equation \(\ref{brdf}\) thusly:
$$\begin{equation} f(v,l)=\fDiffuse(n,l) (1  F_c) + \fSpecular(n,l) (1  F_c) + f_c(n,l) \end{equation}$$
Where \(F_c\) is the Fresnel term of the clear coat BRDF and \(f_c\) the clear coat BRDF
The clear coat material model encompasses all the parameters previously defined for the standard material mode, plus two parameters described in table 6.
Parameter  Definition 

ClearCoat  Strength of the clear coat layer. Scalar between 0 and 1 
ClearCoatRoughness  Perceived smoothness or roughness of the clear coat layer. Scalar between 0 and 1 
The clear coat roughness parameter is remapped and clamped in a similar way to the roughness parameter of the standard material.
Figure 25 and figure 26 show how the clear coat parameters affect the appearance of a surface.
Listing 14 shows the GLSL implementation of the clear coat material model after remapping, parameterization and integration in the standard surface response.
void BRDF(...) {
// compute Fd and Fr from standard model
// remapping and linearization of clear coat roughness
clearCoatPerceptualRoughness = clamp(clearCoatPerceptualRoughness, 0.089, 1.0);
clearCoatRoughness = clearCoatPerceptualRoughness * clearCoatPerceptualRoughness;
// clear coat BRDF
float Dc = D_GGX(clearCoatRoughness, NoH);
float Vc = V_Kelemen(clearCoatRoughness, LoH);
float Fc = F_Schlick(0.04, LoH) * clearCoat; // clear coat strength
float Frc = (Dc * Vc) * Fc;
// account for energy loss in the base layer
return color * ((Fd + Fr * (1.0  Fc)) * (1.0  Fc) + Frc);
}
The presence of a clear coat layer means that we should recompute \(\fNormal\), since it is normally based on an airmaterial interface. The base layer thus requires \(\fNormal\) to be computed based on a clear coatmaterial interface instead.
This can be achieved by computing the material's index of refraction (IOR) from \(\fNormal\), then computing a new \(\fNormal\) based on the newly computed IOR and the IOR of the clear coat layer (1.5).
First, we compute the base layer's IOR:
$$ IOR_{base} = \frac{1 + \sqrt{\fNormal}}{1  \sqrt{\fNormal}} $$
Then we compute the new \(\fNormal\) from this new index of refraction:
$$ f_{0_{base}} = \left( \frac{IOR_{base}  1.5}{IOR_{base} + 1.5} \right) ^2 $$
Since the clear coat layer's IOR is fixed, we can combine both steps to simplify:
$$ f_{0_{base}} = \frac{\left( 1  5 \sqrt{\fNormal} \right) ^2}{\left( 5  \sqrt{\fNormal} \right) ^2} $$
We should also modify the base layer's apparent roughness based on the IOR of the clear coat layer but this is something we have opted to leave out for now.
The standard material model described previously can only describe isotropic surfaces, that is, surfaces whose properties are identical in all directions. Many realworld materials, such as brushed metal, can, however, only be replicated using an anisotropic model.
The isotropic specular BRDF described previously can be modified to handle anisotropic materials. Burley achieves this by using an anisotropic GGX NDF:
$$\begin{equation} D_{aniso}(h,\alpha) = \frac{1}{\pi \alpha_t \alpha_b} \frac{1}{((\frac{t \cdot h}{\alpha_t})^2 + (\frac{b \cdot h}{\alpha_b})^2 + (\NoH)^2)^2} \end{equation}$$
This NDF unfortunately relies on two supplemental roughness terms noted \(\alpha_b\), the roughness along the bitangent direction, and \(\alpha_t\), the roughness along the tangent direction. Neubelt and Pettineo [Neubelt13] propose a way to derive \(\alpha_b\) from \(\alpha_t\) by using an anisotropy parameter that describes the relationship between the two roughness values for a material:
$$ \begin{align*} \alpha_t &= \alpha \\ \alpha_b &= lerp(0, \alpha, 1  anisotropy) \end{align*} $$
The relationship defined in [Burley12] is different, offers more pleasant and intuitive results, but is slightly more expensive:
$$ \begin{align*} \alpha_t &= \frac{\alpha}{\sqrt{1  0.9 \times anisotropy}} \\ \alpha_b &= \alpha \sqrt{1  0.9 \times anisotropy} \end{align*} $$
We instead opted to follow the relationship described in [Kulla17] as it allows creation of sharp highlights:
$$ \begin{align*} \alpha_t &= \alpha \times (1 + anisotropy) \\ \alpha_b &= \alpha \times (1  anisotropy) \end{align*} $$
Note that this NDF requires the tangent and bitangent directions in addition to the normal direction. Since these directions are already needed for normal mapping, providing them may not be an issue.
The resulting implementation is described in listing 15.
float at = max(roughness * (1.0 + anisotropy), 0.001);
float ab = max(roughness * (1.0  anisotropy), 0.001);
float D_GGX_Anisotropic(float NoH, const vec3 h,
const vec3 t, const vec3 b, float at, float ab) {
float ToH = dot(t, h);
float BoH = dot(b, h);
float a2 = at * ab;
highp vec3 v = vec3(ab * ToH, at * BoH, a2 * NoH);
highp float v2 = dot(v, v);
float w2 = a2 / v2;
return a2 * w2 * w2 * (1.0 / PI);
}
In addition, [Heitz14] presents an anisotropic maskingshadowing function to match the heightcorrelated GGX distribution. The maskingshadowing term can be greatly simplified by using the visibility function instead:
$$\begin{equation} G(v,l,h,\alpha) = \frac{\chi^+(\VoH) \chi^+(\LoH)}{1 + \Lambda(v) + \Lambda(l)} \end{equation}$$
$$\begin{equation} \Lambda(m) = \frac{1 + \sqrt{1 + \alpha_0^2 tan^2(\theta_m)}}{2} = \frac{1 + \sqrt{1 + \alpha_0^2 \frac{(1  cos^2(\theta_m))}{cos^2(\theta_m)}}}{2} \end{equation}$$
Where:
$$\begin{equation} \alpha_0 = \sqrt{cos^2(\phi_0)\alpha_x^2 + sin^2(\phi_0)\alpha_y^2} \end{equation}$$
After derivation we obtain:
$$\begin{equation} V_{aniso}(\NoL,\NoV,\alpha) = \frac{1}{2((\NoL)\hat{\Lambda}_v+(\NoV)\hat{\Lambda}_l)} \\ \hat{\Lambda}_v = \sqrt{\alpha^2_t(t \cdot v)^2+\alpha^2_b(b \cdot v)^2+(\NoV)^2} \\ \hat{\Lambda}_l = \sqrt{\alpha^2_t(t \cdot l)^2+\alpha^2_b(b \cdot l)^2+(\NoL)^2} \end{equation}$$
The term \( \hat{\Lambda}_v \) is the same for every light and can be computed only once if needed. The resulting implementation is described in listing 16.
float at = max(roughness * (1.0 + anisotropy), 0.001);
float ab = max(roughness * (1.0  anisotropy), 0.001);
float V_SmithGGXCorrelated_Anisotropic(float at, float ab, float ToV, float BoV,
float ToL, float BoL, float NoV, float NoL) {
float lambdaV = NoL * length(vec3(at * ToV, ab * BoV, NoV));
float lambdaL = NoV * length(vec3(at * ToL, ab * BoL, NoL));
float v = 0.5 / (lambdaV + lambdaL);
return saturateMediump(v);
}
The anisotropic material model encompasses all the parameters previously defined for the standard material mode, plus an extra parameter described in table 7.
Parameter  Definition 

Anisotropy  Amount of anisotropy. Scalar between −1 and 1 
No further remapping is required. Note that negative values will align the anisotropy with the bitangent direction instead of the tangent direction. Figure 28 shows how the anisotropy parameter affect the appearance of a rough metallic surface.
[TODO]
[TODO]
[TODO]
All the material models described previously are designed to simulate dense surfaces, both at a macro and at a micro level. Clothes and fabrics are however often made of loosely connected threads that absorb and scatter incident light. The microfacet BRDFs presented earlier do a poor job of recreating the nature of cloth due to their underlying assumption that a surface is made of random grooves that behave as perfect mirrors. When compared to hard surfaces, cloth is characterized by a softer specular lobe with a large falloff and the presence of fuzz lighting, caused by forward/backward scattering. Some fabrics also exhibit twotone specular colors (velvets for instance).
Figure 29 shows how a traditional microfacet BRDF fails to capture the appearance of a sample of denim fabric. The surface appears rigid (almost plasticlike), more similar to a tarp than a piece of clothing. This figure also shows how important the softer specular lobe caused by absorption and scattering is to the faithful recreation of the fabric.
Velvet is an interesting use case for a cloth material model. As shown in figure 30 this type of fabric exhibits strong rim lighting due to forward and backward scattering. These scattering events are caused by fibers standing straight at the surface of the fabric. When the incident light comes from the direction opposite to the view direction, the fibers will forwardscatter the light. Similarly, when the incident light from the same direction as the view direction, the fibers will scatter the light backward.
Since fibers are flexible, we should in theory model the ability to groom the surface. While our model does not replicate this characteristic, it does model a visible front facing specular contribution that can be attributed to the random variance in the direction of the fibers.
It is important to note that there are types of fabrics that are still best modeled by hard surface material models. For instance, leather, silk and satin can be recreated using the standard or anisotropic material models.
The cloth specular BRDF we use is a modified microfacet BRDF as described by Ashikhmin and Premoze in [Ashikhmin07]. In their work, Ashikhmin and Premoze note that the distribution term is what contributes most to a BRDF and that the shadowing/masking term is not necessary for their velvet distribution. The distribution term itself is an inverted Gaussian distribution. This helps achieve fuzz lighting (forward and backward scattering) while an offset is added to simulate the front facing specular contribution. The socalled velvet NDF is defined as follows:
$$\begin{equation} D_{velvet}(v,h,\alpha) = c_{norm}(1 + 4 exp\left(\frac{{cot}^2\theta_{h}}{\alpha^2}\right)) \end{equation}$$
This NDF is a variant of the NDF the same authors describe in [Ashikhmin00], notably modified to include an offset (set to 1 here) and an amplitude (4). In [Neubelt13], Neubelt and Pettineo propose a normalized version of this NDF:
$$\begin{equation} D_{velvet}(v,h,\alpha) = \frac{1}{\pi(1 + 4\alpha^2)} (1 + 4 \frac{exp\left(\frac{{cot}^2\theta_{h}}{\alpha^2}\right)}{{sin}^4\theta_{h}}) \end{equation}$$
For the full specular BRDF, we also follow [Neubelt13] and replace the traditional denominator with a smoother variant:
$$\begin{equation}\label{clothSpecularBRDF} f_{r}(v,h,\alpha) = \frac{D_{velvet}(v,h,\alpha)}{4(\NoL + \NoV  (\NoL)(\NoV))} \end{equation}$$
The implementation of the velvet NDF is presented in listing 17, optimized to properly fit in half float formats and to avoid computing a costly cotangent, relying instead on trigonometric identities. Note that we removed the Fresnel component from this BRDF.
float D_Ashikhmin(float roughness, float NoH) {
// Ashikhmin 2007, "Distributionbased BRDFs"
float a2 = roughness * roughness;
float cos2h = NoH * NoH;
float sin2h = max(1.0  cos2h, 0.0078125); // 2^(14/2), so sin2h^2 > 0 in fp16
float sin4h = sin2h * sin2h;
float cot2 = cos2h / (a2 * sin2h);
return 1.0 / (PI * (4.0 * a2 + 1.0) * sin4h) * (4.0 * exp(cot2) + sin4h);
}
In [Estevez17] Estevez and Kulla propose a different NDF (called the “Charlie” sheen) that is based on an exponentiated sinusoidal instead of an inverted Gaussian. This NDF is appealing for several reasons: its parameterization feels more natural and intuitive, it provides a softer appearance and, as shown in equation \(\ref{charlieNDF}\), its implementation is simpler:
$$\begin{equation}\label{charlieNDF} D(m) = \frac{(2 + \frac{1}{\alpha}) sin(\theta)^{\frac{1}{\alpha}}}{2 \pi} \end{equation}$$
[Estevez17] also presents a new shadowing term that we omit here because of its cost. We instead rely on the visibility term from [Neubelt13] (shown in equation \(\ref{clothSpecularBRDF}\) above). The implementation of this NDF is presented in listing 18, optimized to properly fit in half float formats.
float D_Charlie(float roughness, float NoH) {
// Estevez and Kulla 2017, "Production Friendly Microfacet Sheen BRDF"
float invAlpha = 1.0 / roughness;
float cos2h = NoH * NoH;
float sin2h = max(1.0  cos2h, 0.0078125); // 2^(14/2), so sin2h^2 > 0 in fp16
return (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);
}
To offer better control over the appearance of cloth and to give users the ability to recreate twotone specular materials, we introduce the ability to directly modify the specular reflectance. Figure 31 shows an example of using the parameter we call “sheen color”.
Our cloth material model still relies on a Lambertian diffuse BRDF. It is however slightly modified to be energy conservative (akin to the energy conservation of our clear coat material model) and offers an optional subsurface scattering term. This extra term is not physically based and can be used to simulate the scattering, partial absorption and reemission of light in certain types of fabrics.
First, here is the diffuse term without the optional subsurface scattering:
$$\begin{equation} f_{d}(v,h) = \frac{c_{diff}}{\pi}(1  F(v,h)) \end{equation}$$
Where \(F(v,h)\) is the Fresnel term of the cloth specular BRDF in equation \(\ref{clothSpecularBRDF}\). In practice we've opted to leave out the \(1  F(v, h)\) term in the diffuse component. The effect is a bit subtle and we deemed it wasn't worth the added cost.
Subsurface scattering is implemented using the wrapped diffuse lighting technique, in its energy conservative form:
$$\begin{equation} f_{d}(v,h) = \frac{c_{diff}}{\pi}(1  F(v,h)) \left< \NoL + \frac{w}{(1 + w)^2} \right> \left< c_{subsurface} + \NoL \right> \end{equation}$$
Where \(w\) is a value between 0 and 1 defining by how much the diffuse light should wrap around the terminator. To avoid introducing another parameter, we fix \(w = 0.5\). Note that with wrap diffuse lighting, the diffuse term must not be multiplied by \(\NoL\). The effect of this cheap subsurface scattering approximation can be seen in figure 32.
The complete implementation of our cloth BRDF, including sheen color and optional subsurface scattering, can be found in listing 19.
// specular BRDF
float D = distributionCloth(roughness, NoH);
float V = visibilityCloth(NoV, NoL);
vec3 F = sheenColor;
vec3 Fr = (D * V) * F;
// diffuse BRDF
float diffuse = diffuse(roughness, NoV, NoL, LoH);
#if defined(MATERIAL_HAS_SUBSURFACE_COLOR)
// energy conservative wrap diffuse
diffuse *= saturate((dot(n, light.l) + 0.5) / 2.25);
#endif
vec3 Fd = diffuse * pixel.diffuseColor;
#if defined(MATERIAL_HAS_SUBSURFACE_COLOR)
// cheap subsurface scatter
Fd *= saturate(subsurfaceColor + NoL);
vec3 color = Fd + Fr * NoL;
color *= (lightIntensity * lightAttenuation) * lightColor;
#else
vec3 color = Fd + Fr;
color *= (lightIntensity * lightAttenuation * NoL) * lightColor;
#endif
The cloth material model encompasses all the parameters previously defined for the standard material mode except for metallic and reflectance. Two extra parameters described in table 8 are also available.
Parameter  Definition 

SheenColor  Specular tint to create twotone specular fabrics (defaults to 0.04 to match the standard reflectance) 
SubsurfaceColor  Tint for the diffuse color after scattering and absorption through the material 
To create a velvetlike material, the base color can be set to black (or a dark color). Chromaticity information should instead be set on the sheen color. To create more common fabrics such as denim, cotton, etc. use the base color for chromaticity and use the default sheen color or set the sheen color to the luminance of the base color.
The correctness and coherence of the lighting environment is paramount to achieving plausible visuals. After surveying existing rendering engines (such as Unity or Unreal Engine 4) as well as the traditional realtime rendering literature, it is obvious that coherency is rarely achieved.
The Unreal Engine, for instance, lets artists specify the “brightness” of a point light in lumens, a unit of luminous power. The brightness of directional lights is however expressed using an arbitrary unnamed unit. To match the brightness of a point light with a luminous power of 5,000 lumens, the artist must use a directional light of brightness 10. This kind of mismatch makes it difficult for artists to maintain the visual integrity of a scene when adding, removing or modifying lights. Using solely arbitrary units is a coherent solution but it makes reusing lighting rigs a difficult task. For instance, an outdoor scene will use a directional light of brightness 10 as the sun and all other lights will be defined relative to that value. Moving these lights to an indoor environment would make them too bright.
Our goal is therefore to make all lighting correct by default, while giving artists enough freedom to achieve the desired look. We will support a number of lights, split in two categories, direct and indirect lighting:
Direct lighting: punctual lights, photometric lights, area lights.
Indirect lighting: image based lights (IBLs), for both local^{2} and distant light probes.
The following sections will discuss how to implement various types of lights and the proposed equations make use of different symbols and units summarized in table 9.
Photometric term  Notation  Unit 

Luminous power  \(\Phi\)  Lumen (\(lm\)) 
Luminous intensity  \(I\)  Candela (\(cd\)) or \(\frac{lm}{sr}\) 
Illuminance  \(E\)  Lux (\(lx\)) or \(\frac{lm}{m^2}\) 
Luminance  \(L\)  Nit (\(nt\)) or \(\frac{cd}{m^2}\) 
Radiant power  \(\Phi_e\)  Watt (\(W\)) 
Luminous efficacy  \(\eta\)  Lumens per watt (\(\frac{lm}{W}\)) 
Luminous efficiency  \(V\)  Percentage (%) 
To get properly coherent lighting, we must use light units that respect the ratio between various light intensities found in realworld scenes. These intensities can vary greatly, from around 800 \(lm\) for a household light bulb to 120,000 \(lx\) for a daylight sky and sun illumination.
The easiest way to achieve lighting coherency is to adopt physical light units. This will in turn enable full reusability of lighting rigs. Using physical light units also allows us to use a physically based camera.
Table 10 shows the light unit associated with each type of light we intend to support.
Light type  Unit 

Directional light  Illuminance (\(lx\) or \(\frac{lm}{m^2}\)) 
Point light  Luminous power (\(lm\)) 
Spot light  Luminous power (\(lm\)) 
Photometric light  Luminous intensity (\(cd\)) 
Masked photometric light  Luminous power (\(lm\)) 
Area light  Luminous power (\(lm\)) 
Image based light  Luminance (\(\frac{cd}{m^2}\)) 
Notes about the radiant power unit
Even though commercially available light bulbs often display their brightness in lumens on the packaging, it is common to refer to the brightness of a light bulb by using its required energy in watts. The number of watts only indicates how much energy a bulb uses, not how bright it is. It is even more important to understand this difference now that more energy efficient bulbs are readily available (halogens, LEDs, etc.).
However, since artists might be accustomed to gauging a light's brightness by its power, we should allow users to use the power unit to define the brightness of a light. The conversion is presented in equation \(\ref{radiantPowerToLuminousPower}\).
$$\begin{equation}\label{radiantPowerToLuminousPower} \Phi = \Phi_e \eta \end{equation}$$
In equation \(\ref{radiantPowerToLuminousPower}\), \(\eta\) is the luminous efficacy of the light, expressed in lumens per watt. Knowing that the maximum possible luminous efficacy is 683 \(\frac{lm}{W}\) we can also use luminous efficiency \(V\) (also called luminous coefficient), as shown in equation \(\ref{radiantPowerLuminousEfficiency}\).
$$\begin{equation}\label{radiantPowerLuminousEfficiency} \Phi = \Phi_e 683 \times V \end{equation}$$
Table 11 can be used as a reference to convert watts to lumens using either the luminous efficacy or the luminous efficiency of various types of lights. More specific values are available on Wikipedia's luminous efficacy page.
Light type  Efficacy \(\eta\)  Efficiency \(V\) 

Incandescent  1435  25% 
LED  28100  415% 
Fluorescent  60100  915% 
One of the big advantages of using physical light units is the ability to physically validate our equations. We can use specialized devices to measure three light units.
The illuminance reaching a surface can be measured using an incident light meter. For our tests, we use a Sekonic L478D, shown in figure 33.
The incident light meter uses a white diffuse dome to capture the illuminance reaching a surface. It is important to orient the dome properly depending on the desired measurement. For instance, orienting the dome perpendicular to the sun on a bright clear day will give very different results than orienting the dome horizontally.
The luminance at a surface, or the product of the incident light and the surface, can be measured using a luminance meter, also often called a spot meter. While incident light meters use a diffuse hemisphere to capture light from all directions, a spot meter uses a shield to measure incident light from a single direction. For our tests, we use a Sekonic 5° Viewfinder that can replace the diffuser on the L478D to measure luminance in a 5° cone.
The luminous intensity of a light source cannot be measured directly but can be derived from the measured illuminance if we know the distance between the measuring device and the light source. Equation \(\ref{derivedLuminousIntensity}\) is a simple application of the inverse square law discussed in section 5.2.2.
$$\begin{equation}\label{derivedLuminousIntensity} I = E \cdot d^2 \end{equation}$$
We have defined the light units for all the light types supported by the renderer in the section above but we have not defined the light unit for the result of the lighting equations. Choosing physical light units means that we will compute luminance values in our shaders, and therefore that all our light evaluation functions will compute the luminance \(L_{out}\) (or outgoing radiance) at any given point. The luminance depends on the illuminance \(E\) and the BSDF \(f(v,l)\) :
$$\begin{equation}\label{luminanceEquation} L_{out} = f(v,l)E \end{equation}$$
The main purpose of directional lights is to recreate important light sources for outdoor environment, i.e. the sun and/or the moon. While directional lights do not truly exist in the physical world, any light source sufficiently far from the light receptor can be assumed to be directional (i.e. all the incident light rays are parallel, as shown in figure 34).
This approximation proves to work incredibly well for the diffuse response of a surface but the specular response is incorrect. The Frostbite engine solves this problem by treating the “sun” directional light as a disc area light. However, our tests have shown that the quality increase does not justify the added computational costs.
We earlier stated that we chose an illuminance light unit (\(lx\)) for directional lights. This is in part due to the fact that we can easily find illuminance values for the sky and the sun (online or with a light meter) but also to simplify the luminance equation described in \(\ref{luminanceEquation}\).
$$\begin{equation}\label{directionalLuminanceEquation} L_{out} = f(v,l) E_{\bot} \left< \NoL \right> \end{equation}$$
In the simplified luminance equation \(\ref{directionalLuminanceEquation}\), \(E_{\bot}\) is the illuminance of the light source for a surface perpendicular to said light source. If the directional light source simulates the sun, \(E_{\bot}\) is the illuminance of the sun for a surface perpendicular to the sun direction.
Table 12 provides useful reference values for the sun and sky illumination, measured^{3} on a clear day in March, in California.
Light  10am  12pm  5:30pm 

\(Sky_{\bot} + Sun_{\bot}\)  120,000  130,000  90,000 
\(Sky_{\bot}\)  20,000  25,000  9,000 
\(Sun_{\bot}\)  100,000  105,000  81,000 
Dynamic directional lights are particularly cheap to evaluate at runtime, as shown in listing 20.
vec3 l = normalize(lightDirection);
float NoL = clamp(dot(n, l), 0.0, 1.0);
// lightIntensity is the illuminance
// at perpendicular incidence in lux
float illuminance = lightIntensity * NoL;
vec3 luminance = BSDF(v, l) * illuminance;
Figure 35 shows the effect of lighting a simple scene with a directional light setup to approximate a midday Sun (illuminance set to 110,000 \(lx\)). For illustration purposes, only direct lighting is shown.
Our engine will support two types of punctual lights, commonly found in most if not all rendering engines: point lights and spot lights. These types of lights are traditionally physically inaccurate for two reasons:
The first issue can be addressed with area lights but, given the cheaper nature of punctual lights it is deemed practical to use infinitesimally small punctual lights whenever possible.
The second issue is easy to fix. For a given punctual light, the perceived intensity decreases proportionally to the square of the distance from the viewer (more precisely, the light receptor).
For punctual lights following the inverse square law, the term \(E\) of equation \( \ref{luminanceEquation} \) is expressed in equation \(\ref{punctualLightEquation}\), where \(d\) is the distance from a point at the surface to the light.
$$\begin{equation}\label{punctualLightEquation} E = L_{in} \left< \NoL \right> = \frac{I}{d^2} \left< \NoL \right> \end{equation}$$
The difference between point and spot lights lies in how \(E\) is computed, and in particular how the luminous intensity \(I\) is computed from the luminous power \(\Phi\).
A point light is defined only by a position in space, as shown in figure 36.
The luminous power of a point light is calculated by integrating the luminous intensity over the light's solid angle, as show in equation \(\ref{pointLightLuminousPower}\). The luminous intensity can then be easily derived from the luminous power.
$$\begin{equation}\label{pointLightLuminousPower} \Phi = \int_{\Omega} I dl = \int_{0}^{2\pi} \int_{0}^{\pi} I d\theta d\phi = 4 \pi I \\ I = \frac{\Phi}{4 \pi} \end{equation}$$
By simple substitution of \(I\) in \(\ref{punctualLightEquation}\) and \(E\) in \( \ref{luminanceEquation} \) we can formulate the luminance equation of a point light as a function of the luminous power (see \( \ref{pointLightLuminanceEquation} \)).
$$\begin{equation}\label{pointLightLuminanceEquation} L_{out} = f(v,l) \frac{\Phi}{4 \pi d^2} \left< \NoL \right> \end{equation}$$
Figure 37 shows the effect of lighting a simple scene with a point light subject to distance attenuation. Light falloff is exaggerated for illustration purposes.
A spot light is defined by a position in space, a direction vector and two cone angles, \( \theta_{inner} \) and \( \theta_{outer} \) (see figure 38). These two angles are used to define the angular falloff attenuation of the spot light. The light evaluation function of a spot light must therefore take into account both the inverse square law and these two angles to properly evaluate the luminance attenuation.
Equation \( \ref{spotLightLuminousPower} \) describes how the luminous power of a spot light can be calculated in a similar fashion to point lights, using \( \theta_{outer} \) the outer angle of the spot light's cone in the range [0..\(\pi\)].
$$\begin{equation}\label{spotLightLuminousPower} \Phi = \int_{\Omega} I dl = \int_{0}^{2\pi} \int_{0}^{\theta_{outer}} I d\theta d\phi = 2 \pi (1  cos\frac{\theta_{outer}}{2})I \\ I = \frac{\Phi}{2 \pi (1  cos\frac{\theta_{outer}}{2})} \end{equation}$$
While this formulation is physically correct, it makes spot lights a little difficult to use: changing the outer angle of the cone changes the illumination levels. Figure 39 shows the same scene lit by a spot light, with an outer angle of 55° and an outer angle of 15°. Observes how the illumination level increases as the cone aperture decreases.
The coupling of illumination and the outer cone means that an artist cannot tweak the influence cone of a spot light without also changing the perceived illumination. It therefore makes sense to provide artists with a parameter to disable this coupling. Equations \( \ref{spotLightLuminousPowerB} \) shows how to formulate the luminous power for that purpose.
$$\begin{equation}\label{spotLightLuminousPowerB} \Phi = \pi I \\ I = \frac{\Phi}{\pi} \\ \end{equation}$$
With this new formulation to compute the luminous intensity, the test scene in figure 40 exhibits similar illumination levels with both cone apertures.
This new formulation can also be considered physically based if the spot's reflector is replaced with a matte, diffuse mask that absorbs light perfectly.
The spot light evaluation function can be expressed in two ways:
The term \( \lambda(l) \) in equations \( \ref{spotAbsorber} \) and \( \ref{spotReflector} \) is the spot's angle attenuation factor described in equation \( \ref{spotAngleAtt} \) below.
$$\begin{equation}\label{spotAngleAtt} \lambda(l) = \frac{l \times spotDirection  cos\theta_{outer}}{cos\theta_{inner}  cos\theta_{outer}} \end{equation}$$
A proper evaluation of the inverse square law attenuation factor is mandatory for physically based punctual lights. The simple mathematical formulation is unfortunately impractical for implementation purposes:
The first issue can be solved easily by setting the assumption that punctual lights are not truly punctual but instead small area lights. To do this we can simply treat punctual lights as spheres of 1 cm radius, as show in equation \(\ref{finitePunctualLight}\).
$$\begin{equation}\label{finitePunctualLight} E = \frac{I}{max(d^2, {0.01}^2)} \end{equation}$$
We can solve the second issue by introducing an influence radius for each light. There are several advantages to this solution. Tools can quickly show artists what parts of the world will be influenced by every light (the tool just needs to draw a sphere centered on each light). The rendering engine can cull lights more aggressively using this extra piece of information and artists/developers can assist the engine by manually tweaking the influence radius of a light.
Mathematically, the illuminance of a light should smoothly reach zero at the limit defined by the influence radius. [Karis13] proposes to window the inverse square function in such a way that the majority of the light's influence remains unaffected. The proposed windowing is described in equation \(\ref{attenuationWindowing}\), where \(r\) is the light's radius of influence.
$$\begin{equation}\label{attenuationWindowing} E = \frac{I}{max(d^2, {0.01}^2)} \left< 1  \frac{d^4}{r^2} \right> \end{equation}$$
Listing 21 demonstrates how to implement physically based punctual lights in GLSL. Note that the light intensity used in this piece of code is the luminous intensity \(I\) in \(cd\), converted from the luminous power CPUside. This snippet is not optimized and some of the computations can be offloaded to the CPU (for instance the square of the light's inverse falloff radius, or the spot scale and angle).
float getSquareFalloffAttenuation(vec3 posToLight, float lightInvRadius) {
float distanceSquare = dot(posToLight, posToLight);
float factor = distanceSquare * lightInvRadius * lightInvRadius;
float smoothFactor = max(1.0  factor * factor, 0.0);
return (smoothFactor * smoothFactor) / max(distanceSquare, 1e4);
}
float getSpotAngleAttenuation(vec3 l, vec3 lightDir,
float innerAngle, float outerAngle) {
// the scale and offset computations can be done CPUside
float cosOuter = cos(outerAngle);
float spotScale = 1.0 / max(cos(innerAngle)  cosOuter, 1e4)
float spotOffset = cosOuter * spotScale
float cd = dot(normalize(lightDir), l);
float attenuation = clamp(cd * spotScale + spotOffset, 0.0, 1.0);
return attenuation * attenuation;
}
vec3 evaluatePunctualLight() {
vec3 l = normalize(posToLight);
float NoL = clamp(dot(n, l), 0.0, 1.0);
vec3 posToLight = lightPosition  worldPosition;
float attenuation;
attenuation = getSquareFalloffAttenuation(posToLight, lightInvRadius);
attenuation *= getSpotAngleAttenuation(l, lightDir, innerAngle, outerAngle);
vec3 luminance = (BSDF(v, l) * lightIntensity * attenuation * NoL) * lightColor;
return luminance;
}
Punctual lights are an extremely practical and efficient way to light a scene but do not give artists enough control over the light distribution. The field of architectural lighting design concerns itself with designing lighting systems to serve humans needs by taking into account:
The lighting system we have described so far can easily address the first two points but we need a way to define the distribution of light within the space. Light distribution is especially important for indoor scenes or for some types of outdoor scenes or even road lighting. Figure 41 shows scenes where the light distribution is controlled by the artist. This type of distribution control is widely used when putting objects on display (museums, stores or galleries for instance).
Photometric lights use a photometric profile to describe their intensity distribution. There are two commonly used formats, IES (Illuminating Engineering Society) and EULUMDAT (European Lumen Data format) but we will focus on the former. IES profiles are supported by many tools and engines, such as Unreal Engine 4, Frostbite, Renderman, Maya and Killzone. In addition, IES light profiles are commonly made available by bulbs and luminaires manufacturers (Philips offers an extensive array of IES files for download for instance). Photometric profiles are particularly useful when they measure a luminaire or light fixture, in which the light source is partially covered. The luminaire will block the light emitted in certain directions, thus shaping the light distribution.
An IES profile stores luminous intensity for various angles on a sphere around the measured light source. This spherical coordinate system is usually referred to as the photometric web, which can be visualized using specialized tools such as IESviewer. Figure 42 below shows the photometric web of the XArrow IES profile provided by Pixar for use with Renderman. This picture also shows a rendering in 3D space of the XArrow IES profile by our tool lightgen
.
The IES format is poorly documented and it is not uncommon to find syntax variations between files found on the Internet. The best resource to understand IES profile is Ian Ashdown's “Parsing the IESNA LM63 photometric data file” document [Ashdown98]. Succinctly, an IES profiles stores luminous intensities in candela at various angles around the light source. For each measured horizontal angle, a series of luminous intensities at different vertical angles is provided. It is however fairly common for measured light sources to be horizontally symmetrical. The XArrow profile shown above is a good example: intensities vary with vertical angles (vertical axis) but are symmetrical on the horizontal axis. The range of vertical angles in an IES profile is 0 to 180° and the range of horizontal angles is 0 to 360°.
Figure 43 shows the series of IES profiles provided by Pixar for Renderman, rendered using our lightgen
tool.
IES profiles can be applied directly to any punctual light, point or spot. To do so, we must first process the IES profile and generate a photometric profile as a texture. For performance considerations, the photometric profile we generate is a 1D texture that represents the average luminous intensity for all horizontal angles at a specific vertical angle (i.e., each pixel represents a vertical angle). To truly represent a photometric light, we should use a 2D texture but since most lights are fully, or mostly, symmetrical on the horizontal plane, we can accept this approximation. The values stored in the texture are normalized by the inverse maximum intensity defined in the IES profile. This allows us to easily store the texture in any float format or, at the cost of a bit of precision, in a luminance 8bit texture (grayscale PNG for instance). Storing normalized values also allows us to treat photometric profiles as a mask:
The luminous intensity is defined by the artist by setting the luminous power of the light, as with any other punctual light. The artist defined intensity is divided by the intensity of the light computed from the IES profile. IES profiles contain a luminous intensity but it is only valid for a bare light bulb whereas the measured intensity values take into account the light fixture. To measure the intensity of the luminaire, instead of the bulb, we perform a MonteCarlo integration of the unit sphere using the intensities from the profile^{4}.
The luminous intensity comes from the profile itself. All the values sampled from the 1D texture are simply multiplied by the maximum intensity. We also provide a multiplier for convenience.
$$\begin{equation}\label{photometricLightEvaluation} L_{out} = f(v,l) \frac{I}{d^2} \left< \NoL \right> \Psi(l) \end{equation}$$
The term \( \Psi(l) \) is the photometric attenuation function. It depends on the light vector, but also on the direction of the light. Spot lights already possess a direction vector but we need to introduce one for photometric point lights as well.
The photometric attenuation function can be easily implemented in GLSL by adding a new attenuation factor to the implementation of punctual lights (listing 21). The modified implementation is show in listing 22.
float getPhotometricAttenuation(vec3 posToLight, vec3 lightDir) {
float cosTheta = dot(posToLight, lightDir);
float angle = acos(cosTheta) * (1.0 / PI);
return texture2DLodEXT(lightProfileMap, vec2(angle, 0.0), 0.0).r;
}
vec3 evaluatePunctualLight() {
vec3 l = normalize(posToLight);
float NoL = clamp(dot(n, l), 0.0, 1.0);
vec3 posToLight = lightPosition  worldPosition;
float attenuation;
attenuation = getSquareFalloffAttenuation(posToLight, lightInvRadius);
attenuation *= getSpotAngleAttenuation(l, lightDirection, innerAngle, outerAngle);
attenuation *= getPhotometricAttenuation(l, lightDirection);
float luminance = (BSDF(v, l) * lightIntensity * attenuation * NoL) * lightColor;
return luminance;
}
The light intensity is computed CPUside (listing 23) and depends on whether the photometric profile is used as a mask.
float multiplier;
// Photometric profile used as a mask
if (photometricLight.isMasked()) {
// The desired intensity is set by the artist
// The integrated intensity comes from a MonteCarlo
// integration over the unit sphere around the luminaire
multiplier = photometricLight.getDesiredIntensity() /
photometricLight.getIntegratedIntensity();
} else {
// Multiplier provided for convenience, set to 1.0 by default
multiplier = photometricLight.getMultiplier();
}
// The max intensity in cd comes from the IES profile
float lightIntensity = photometricLight.getMaxIntensity() * multiplier;
[TODO]
Similarly to the parameterization of the standard material model, our goal is to make lights parameterization intuitive and easy to use for artists and developers alike. In that spirit, we decided to separate the light color (or hue) from the light intensity. A light color will therefore be defined as a linear RGB color (or sRGB in the tools UI for convenience).
The full list of light parameters is presented in table 13.
Parameter  Definition 

Type  Directional, point, spot or area 
Direction  Used for directional lights, spot lights, photometric point lights, and linear and tubular area lights (orientation) 
Color  The color of emitted light, as a linear RGB color. Can be specified as an sRGB color or a color temperature in the tools 
Intensity  The light's brightness. The unit depends on the type of light 
Falloff radius  Maximum distance of influence 
Inner angle  Angle of the inner cone for spot lights, in degrees 
Outer angle  Angle of the outer cone for spot lights, in degrees 
Length  Length of the area light, used to create linear or tubular lights 
Radius  Radius of the area light, used to create spherical or tubular lights 
Photometric profile  Texture representing a photometric light profile, works only for punctual lights 
Masked profile  Boolean indicating whether the IES profile is used as a mask or not. When used as a mask, the light's brightness will be multiplied by the ratio between the user specified intensity and the integrated IES profile intensity. When not used as a mask, the user specified intensity is ignored but the IES multiplier is used instead 
Photometric multiplier  Brightness multiplier for photometric lights (if IES as mask is turned off) 
Note: to simplify the implementation, all luminous powers will converted to luminous intensities (\(cd\)) before being sent to the shader. The conversion is light dependent and is explained in the previous sections.
Note: the light type can be inferred from other parameters (e.g. a point light has a length, radius, inner angle and outer angle of 0).
However, realworld artificial lights are often defined by their color temperature, measured in Kelvin (K). The color temperature of a light source is the temperature of an ideal blackbody radiator that radiates light of comparable hue to that of the light source. For convenience, the tools should allow the artist to specify the hue of a light source as a color temperature (a meaningful range is 1,000 K to 12,500 K).
To compute RGB values from a temperature, we can use the Planckian locus, shown in figure 44. This locus is the path that the color of an incandescent black body takes in a chromaticity space as the body's temperature changes.
The easiest way to compute RGB values from this locus is to use the formula described in [Krystek85]. Krystek's algorithm (equation \(\ref{krystek}\)) works in the CIE 1960 (UCS) space, using the following formula where \(T\) is the desired temperature, and \(u\) and \(v\) the coordinates in UCS.
$$\begin{equation}\label{krystek} u(T) = \frac{0.860117757 + 1.54118254 \times 10^{4}T + 1.28641212 \times 10^{7}T^2}{1 + 8.42420235 \times 10^{4}T + 7.08145163 \times 10^{7}T^2} \\ v(T) = \frac{0.317398726 + 4.22806245 \times 10^{5}T + 4.20481691 \times 10^{8}T^2}{1  2.89741816 \times 10^{5}T + 1.61456053 \times 10^{7}T^2} \end{equation}$$
This approximation is accurate to roughly \( 9 \times 10^{5} \) in the range 1,000K to 15,000K. From the CIE 1960 space we can compute the coordinates in xyY space (CIES 1931), using the formula from equation \(\ref{cieToxyY}\).
$$\begin{equation}\label{cieToxyY} x = \frac{3u}{2u  8v + 4} \\ y = \frac{2v}{2u  8v + 4} \end{equation}$$
The formulas above are valid for black body color temperatures, and therefore correlated color temperatures of standard illuminants. If we wish to compute the precise chromaticity coordinates of standard CIE illuminants in the D series we can use equation \(\ref{seriesDtoxyY}\).
$$\begin{equation}\label{seriesDtoxyY} x = \begin{cases} 0.244063 + 0.09911 \frac{10^3}{T} + 2.9678 \frac{10^6}{T^2}  4.6070 \frac{10^9}{T^3} & 4,000K \le T \le 7,000K \\ 0.237040 + 0.24748 \frac{10^3}{T} + 1.9018 \frac{10^6}{T^2}  2.0064 \frac{10^9}{T^3} & 7,000K \le T \le 25,000K \end{cases} \\ y = 3x^2 + 2.87 x  0.275 \end{equation}$$
From the xyY space, we can then convert to the CIE XYZ space (equation \(\ref{xyYtoXYZ}\)).
$$\begin{equation}\label{xyYtoXYZ} X = \frac{xY}{y} \\ Z = \frac{(1  x  y)Y}{y} \end{equation}$$
For our needs, we will fix \(Y = 1\). This allows us to convert from the XYZ space to linear RGB with a simple 3×3 matrix, as shown in equation \(\ref{XYZtoRGB}\).
$$\begin{equation}\label{XYZtoRGB} \left[ \begin{matrix} R \\ G \\ B \end{matrix} \right] = M^{1} \left[ \begin{matrix} X \\ Y \\ Z \end{matrix} \right] \end{equation}$$
The transformation matrix M is calculated from the target RGB color space primaries. Equation \( \ref{XYZtoRGBValues} \) shows the conversion using the inverse matrix for the sRGB color space.
$$\begin{equation}\label{XYZtoRGBValues} \left[ \begin{matrix} R \\ G \\ B \end{matrix} \right] = \left[ \begin{matrix} 3.2404542 & 1.5371385 & 0.4985314 \\ 0.9692660 & 1.8760108 & 0.0415560 \\ 0.0556434 & 0.2040259 & 1.0572252 \end{matrix} \right] \left[ \begin{matrix} X \\ Y \\ Z \end{matrix} \right] \end{equation}$$
The result of these operations is a linear RGB triplet in the sRGB color space. Since we care about the chromaticity of the results, we must apply a normalization step to avoid clamping values greater than 1.0 and distort resulting colors:
$$\begin{equation}\label{normalizedRGB} \hat{C}_{linear} = \frac{C_{linear}}{max(C_{linear})} \end{equation}$$
We must finally apply the sRGB optoelectronic conversion function (OECF, shown in equation \( \ref{OECFsRGB} \)) to obtain a displayable value (the value should remain linear if passed to the renderer for shading).
$$\begin{equation}\label{OECFsRGB} C_{sRGB} = \begin{cases} 12.92 \times \hat{C}_{linear} & \hat{C}_{linear} \le 0.0031308 \\ 1.055 \times \hat{C}_{linear}^{\frac{1}{2.4}}  0.055 & \hat{C}_{linear} \gt 0.0031308 \end{cases} \end{equation}$$
For convenience, figure 45 shows the range of correlated color temperatures from 1,000K to 12,500K. All the colors used below assume CIE \( D_{65} \) as the white point (as is the case in the sRGB color space).
Similarly, figure 46 shows the range of CIE standard illuminants series D from 1,000K to 12,500K.
For reference, figure 47 shows the range of correlated color temperatures without the normalization step presented in equation \(\ref{normalizedRGB}\).
Table 14 presents the correlated color temperature of various common light sources as sRGB color swatches. These colors are relative to the \( D_{65} \) white point, so their perceived hue might vary based on your display's white point. See What colour is the Sun? for more information.
Temperature (K)  Light source  Color 

1,7001,800  Match flame  
1,8501,930  Candle flame  
2,0003,000  Sun at sunrise/sunset  
2,5002,900  Household tungsten lightbulb  
3,000  Tungsten lamp 1K  
3,2003,500  Quartz lights  
3,2003,700  Fluorescent lights  
3,275  Tungsten lamp 2K  
3,380  Tungsten lamp 5K, 10K  
5,0005,400  Sun at noon  
5,5006,500  Daylight (sun + sky)  
5,5006,500  Sun through clouds/haze  
6,0007,500  Overcast sky  
6,500  RGB monitor white point  
7,0008,000  Shaded areas outdoors  
8,00010,000  Partly cloudy sky  
Physically based rendering and physical light units pose an interesting challenge: how to store and handle the large range of values produced by the lighting code? Assuming computations performed at full precision in the shaders, we still want to be able to store the linear output of the lighting pass in a reasonably sized buffer (RGB16F
or equivalent). The most obvious and easiest way to achieve this is to simply apply the camera exposure (see the Physically based camera section for more information) before writing out the result of the lighting pass. This simple step is shown in listing 24:
fragColor = luminance * camera.exposure;
This solution solves the storage problem but requires intermediate computations to be performed with single precision floats. We would instead prefer to perform all (or at least most) of the lighting work using half precision floats instead. Doing so can greatly improve performance and power usage, particularly on mobile devices. Half precision floats are however illsuited for this kind of work as common illuminance and luminance values (for the sun for instance) can exceed their range. The solution is to simply preexpose the lights themselves instead of the result of the lighting pass. This can be done efficiently on the CPU if updating a light's constant buffer is cheap. This can also be done on the GPU, as shown in listing 25.
// The inputs must be highp/single precision,
// both for range (intensity) and precision (exposure)
// The output is mediump/half precision
float computePreExposedIntensity(highp float intensity, highp float exposure) {
return intensity * exposure;
}
Light getPointLight(uint index) {
Light light;
uint lightIndex = // fetch light index;
// the intensity must be highp/single precision
highp vec4 colorIntensity = lightsUniforms.lights[lightIndex][1];
// preexpose the light
light.colorIntensity.w = computePreExposedIntensity(
colorIntensity.w, frameUniforms.exposure);
return light;
}
In practice we preexpose the following lights:
In real life, light comes from every direction either directly from light sources or indirectly after bouncing off objects in the environment, being partially absorbed in the process. In a way the whole environment around an object can be seen as a light source. Images, in particular cubemaps, are a great way to encode such an “environment light”. This is called Image Based Lighting (IBL) or sometimes Indirect Lighting.
There are limitations with imagebased lighting. Obviously the environment image must be acquired somehow and as we'll see below it needs to be preprocessed before it can be used for lighting. Typically, the environment image is acquired offline in the real world, or generated by the engine either offline or at run time; either way, local or distant probes are used.
These probes can be used to acquire the distant or local environment. In this document, we're focusing on distant environment probes, where the light is assumed to come from infinitely far away (which means every point on the object's surface uses the same environment map).
The whole environment contributes light to a given point on the object's surface; this is called irradiance (\(E\)). The resulting light bouncing off of the object is called radiance (\(L_{out}\)). Incident lighting must be applied consistently to the diffuse and specular parts of the BRDF.
The radiance \(L_{out}\) resulting from the interaction between an image based light's (IBL) irradiance and a material model (BRDF) \(f(\Theta)\)^{5} is computed as follows:
$$\begin{equation} L_{out}(n, v, \Theta) = \int_\Omega f(l, v, \Theta) L_{\bot}(l) \left< \NoL \right> dl \end{equation}$$
Note that here we're looking at the behavior of the surface at macro level (not to be confused with the micro level equation), which is why it only depends on \(\vec n\) and \(\vec v\). Essentially, we're applying the BRDF to “pointlights” coming from all directions and encoded in the IBL.
There are four common types of IBLs used in modern rendering engines:
In addition we must distinguish between static and dynamic IBLs. Implementing a fully dynamic day/night cycle requires for instance to recompute the distant light probes dynamically^{6}. Both planar and screen space reflections are inherently dynamic.
As discussed previously in the direct lighting section, all our lights must use physical units. As such our IBLs will use the luminance unit \(\frac{cd}{m^2}\), which is also the output unit of all our direct lighting equations. Using the luminance unit is straightforward for light probes captures by the engine (dynamically or statically offline).
High dynamic range images are a bit more delicate to handle however. Cameras do not record measured luminance but a devicedependent value that is only related to the original scene luminance. As such, we must provide artists with a multiplier that allows them to recover, or at the very least closely approximate, the original absolute luminance.
To properly reconstruct the luminance of an HDRI for IBL, artists must do more than simply take photos of the environment and record extra information:
[TODO] Measure and list common luminance values (clear sky, interior, etc.)
We saw previously that the radiance of an IBL is computed by integrating over the surface's hemisphere. Since this would obviously be too expensive to do in realtime, we must first preprocess our light probes to convert them into a format better suited for realtime interactions.
The sections below will discuss the techniques used to accelerate the evaluation of light probes:
Using the Lambertian BRDF^{7}, we get the radiance:
$$ \begin{align*} f_d(\sigma) &= \frac{\sigma}{\pi} \\ L_d(n, \sigma) &= \int_{\Omega} f_d(\sigma) L_{\bot}(l) \left< \NoL \right> dl \\ &= \frac{\sigma}{\pi} \int_{\Omega} L_{\bot}(l) \left< \NoL \right> dl \\ &= \frac{\sigma}{\pi} E_d(n) \quad \text{with the irradiance} \; E_d(n) = \int_{\Omega} L_{\bot}(l) \left< \NoL \right> dl \end{align*} $$
Or in the discrete domain:
$$ E_d(n) \equiv \sum_{\forall \, i \in image} L_{\bot}(s_i) \left< n \cdot s_i \right> \Omega_s $$
\(\Omega_s\) is the solidangle^{8} associated to sample \(i\).
The irradiance integral \(\Ed\) can be trivially, albeit slowly^{9}, precomputed and stored into a cubemap for efficient access at runtime. Typically, image is a cubemap or an equirectangular image. The term \( \frac{\sigma}{\pi} \) is independent of the IBL and is added at runtime to obtain the radiance.
However, the irradiance can also be approximated very closely by a decomposition into Spherical Harmonics (SH, described in more details in the Spherical Harmonics section) and calculated at runtime cheaply. It is usually best to avoid texture fetches on mobile and freeup a texture unit. Even if it is stored into a cubemap, it is orders of magnitude faster to precompute the integral using SH decomposition followed by a rendering.
SH decomposition is similar in concept to a Fourier transform, it expresses the signal over an orthonormal base in the frequency domain. The properties that interests us most are:
In practice only 4 or 9 coefficients (i.e.: 2 or 3 bands) are enough for \(\cosTheta\) meaning we don't need more either for \(\Lt\).
In practice we preconvolve \(\Lt\) with \(\cosTheta\) and prescale these coefficients by the basis scaling factors \(K_l^m\) so that the reconstruction code is as simple as possible in the shader:
vec3 irradianceSH(vec3 n) {
// uniform vec3 sphericalHarmonics[9]
// We can use only the first 2 bands for better performance
return
sphericalHarmonics[0]
+ sphericalHarmonics[1] * (n.y)
+ sphericalHarmonics[2] * (n.z)
+ sphericalHarmonics[3] * (n.x)
+ sphericalHarmonics[4] * (n.y * n.x)
+ sphericalHarmonics[5] * (n.y * n.z)
+ sphericalHarmonics[6] * (3.0 * n.z * n.z  1.0)
+ sphericalHarmonics[7] * (n.z * n.x)
+ sphericalHarmonics[8] * (n.x * n.x  n.y * n.y);
}
Note that with 2 bands, the computation above becomes a single \(4 \times 4\) matrixbyvector multiply.
Additionally, because of the prescaling by \(K_l^m\), the SH coefficients can be thought of as colors, in particular sphericalHarmonics[0]
is directly the average irradiance.
As we've seen above, the radiance \(\Lout\) resulting from the interaction between an IBL's irradiance and a BRDF is:
$$\begin{equation}\label{specularBRDFIntegration} \Lout(n, v, \Theta) = \int_\Omega f(l, v, \Theta) \Lt(l) \left< \NoL \right> \partial l \end{equation}$$
We recognize the convolution of \(\Lt\) by \(f(l, v, \Theta) \left< \NoL \right>\), i.e.: the environment is filtered using the BRDF as a kernel. Indeed at higher roughness, specular reflections look more blurry.
Plugging the expression of \(f\) in equation \(\ref{specularBRDFIntegration}\), we obtain:
$$\begin{equation} \Lout(n,v,\Theta) = \int_\Omega D(l, v, \alpha) F(l, v, f_0, f_{90}) V(l, v, \alpha) \left< \NoL \right> \Lt(l) \partial l \end{equation}$$
This expression depends on \(v\), \(\alpha\), \(f_0\) and \(f_{90}\) inside the integral, which makes its evaluation extremely costly and unsuitable for realtime on mobile (even using prefiltered importance sampling).
Since there is no closedform solution or an easy way to compute the \(\Lout\) integral, we use a simplified equation instead: \(\hat{I}\), whereby we assume that \(v = n\), that is the view direction \(v\) is always equal to the surface normal \(n\). Clearly, this assumption will break all viewdependant effects of the convolution, such as the increased blur in reflections closer to the viewer (a.k.a. stretchy reflections).
Such a simplification would also have a severe impact on constant environments, such as the white furnace, because it would affect the magnitude of the constant (i.e. DC) term of the result. We can at least correct for that by using a scale factor, \(K\), in our simplified integral, which will make sure the average irradiance stay correct when chosen properly.
Because \(I\) is an integral multiplications can be distributed over it. i.e.: \(I(g()f()) = I(g())I(f())\).
Armed with that,
$$\begin{equation} I( f(\Theta) \Lt ) \approx \tilde{I}( f(\Theta) \Lt ) \\ \tilde{I}( f(\Theta) \Lt ) = K \times \hat{I}( f(\Theta) \Lt ) \\ K = \frac{I(f(\Theta))}{\hat{I}(f(\Theta))} \end{equation}$$
From the equation above we can see that \(\tilde{I}\) is equivalent to \(I\) when \(\Lt\) is a constant, and yields the correct result:
$$\begin{align*} \tilde{I}(f(\Theta)\Lt^{constant}) &= \Lt^{constant} \hat{I}(f(\Theta)) \frac{I(f(\Theta))}{\hat{I}(f(\Theta))} \\ &= \Lt^{constant} I(f(\Theta)) \\ &= I(f(\Theta)\Lt^{constant}) \end{align*}$$
Similarly, we can also demonstrate that the result is correct when \(v = n\), since in that case \(I = \hat{I}\):
$$\begin{align*} \tilde{I}(f(\Theta)\Lt) &= I(f(\Theta)\Lt) \frac{I(f(\Theta))}{I(f(\Theta))} \\ &= I(f(\Theta)\Lt) \end{align*}$$
Finally, we can show that the scale factor \(K\) satisfies our average irradiance (\(\bar{\Lt}\)) requirement by plugging \(\Lt = \bar{\Lt} + (\Lt  \bar{\Lt}) = \bar{\Lt} + \Delta\Lt\) into \(\tilde{I}\):
$$\begin{align*} \tilde{I}(f(\Theta)\Lt) &= \tilde{I}\left[f\left(\Theta\right) \left(\bar{\Lt} + \Delta\Lt\right)\right] \\ &= K \times \hat{I}\left[f\left(\Theta\right) \left(\bar{\Lt} + \Delta\Lt\right)\right] \\ &= K \times \left[\hat{I}\left(f\left(\Theta\right)\bar{\Lt}\right) + \hat{I}\left(f\left(\Theta\right)\Delta\Lt\right)\right] \\ &= K \times \hat{I}\left(f\left(\Theta\right)\bar{\Lt}\right) + K \times \hat{I}\left(f\left(\Theta\right) \Delta\Lt\right) \\ &= \tilde{I}\left(f\left(\Theta\right)\bar{\Lt}\right) + \tilde{I}\left(f\left(\Theta\right) \Delta\Lt\right) \\ &= I\left(f\left(\Theta\right)\bar{\Lt}\right) + \tilde{I}\left(f\left(\Theta\right) \Delta\Lt\right) \end{align*}$$
The above result shows that the average irradiance is computed correctly, i.e.: \(I(f(\Theta)\bar{\Lt})\).
A way to think about this approximation is that it splits the radiance \(\Lt\) in two parts, the average \(\bar{\Lt}\) and the delta from the average \(\Delta\Lt\) and computes the correct integration of the average part then adds the simplified integration of the delta part:
$$\begin{equation} approximation(\Lt) = correct(\bar{\Lt}) + simplified(\Lt  \bar{\Lt}) \end{equation}$$
Now, let's look at each term:
$$\begin{equation}\label{iblPartialEquations} \hat{I}(f(n, \alpha) \Lt) = \int_\Omega f(l, n, \alpha) \Lt(l) \left< \NoL \right> \partial l \\ \hat{I}(f(n, \alpha)) = \int_\Omega f(l, n, \alpha) \left< \NoL \right> \partial l \\ I(f(n, v, \alpha)) = \int_\Omega f(l, n, v, \alpha) \left< \NoL \right> \partial l \end{equation}$$
All three of these equations can be easily precalculated and stored in lookup tables, as explained below.
In the discrete domain the equations in \ref{iblPartialEquations} become:
$$\begin{equation} \hat{I}(f(n, \alpha) \Lt) \equiv \frac{1}{N}\sum_{\forall \, i \in image} f(l_i, n, \alpha) \Lt(l_i) \left<\NoL\right> \\ \hat{I}(f(n, \alpha)) \equiv \frac{1}{N}\sum_{\forall \, i \in image} f(l_i, n, \alpha) \left<\NoL\right> \\ I(f(n, v, \alpha)) \equiv \frac{1}{N}\sum_{\forall \, i \in image} f(l_i, n, v, \alpha) \left<\NoL\right> \end{equation}$$
However, in practice we're using importance sampling which needs to take the \(pdf\) of the distribution into account and adds a term \(\frac{\left<\VoH\right>}{D(h_i, \alpha)\left<\NoH\right>}\). See Importance Sampling For The IBL section:
$$\begin{equation}\label{iblImportanceSampling} \hat{I}(f(n, \alpha) \Lt) \equiv \frac{4}{N}\sum_i^N f(l_i, n, \alpha) \frac{\left<\VoH\right>}{D(h_i, \alpha)\left<\NoH\right>} \Lt(l_i) \left<\NoL\right> \\ \hat{I}(f(n, \alpha)) \equiv \frac{4}{N}\sum_i^N f(l_i, n, \alpha) \frac{\left<\VoH\right>}{D(h_i, \alpha)\left<\NoH\right>} \left<\NoL\right> \\ I(f(n, v, \alpha)) \equiv \frac{4}{N}\sum_i^N f(l_i, n, v, \alpha) \frac{\left<\VoH\right>}{D(h_i, \alpha)\left<\NoH\right>} \left<\NoL\right> \end{equation}$$
Recalling that for \(\hat{I}\), we assume that \(v = n\), equations \ref{iblImportanceSampling}, simplifies to:
$$\begin{equation} \hat{I}(f(n, \alpha) \Lt) \equiv \frac{4}{N}\sum_i^N \frac{f(l_i, n, \alpha)}{D(h_i, \alpha)} \Lt(l_i) \left<\NoL\right> \\ \hat{I}(f(n, \alpha)) \equiv \frac{4}{N}\sum_i^N \frac{f(l_i, n, \alpha)}{D(h_i, \alpha)} \left<\NoL\right> \\ I(f(n, v, \alpha)) \equiv \frac{4}{N}\sum_i^N \frac{f(l_i, n, v, \alpha)}{D(h_i, \alpha)} \frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \end{equation}$$
Then, the first two equations can be merged together such that \(LD(n, \alpha) = \frac{\hat{I}(f(n, \alpha) \Lt)}{\hat{I}(f(n, \alpha))}\)
$$\begin{equation}\label{iblLD} LD(n, \alpha) \equiv \frac{\sum_i^N \frac{f(l_i, n, \alpha)}{D(h_i, \alpha)} \Lt(l_i) \left<\NoL\right>}{\sum_i^N \frac{f(l_i, n, \alpha)}{D(h_i, \alpha)}\left<\NoL\right>} \end{equation}$$ $$\begin{equation}\label{iblDFV} I(f(n, v, \alpha)) \equiv \frac{4}{N}\sum_i^N \frac{f(l_i, n, v, \alpha)}{D(h_i, \alpha)} \frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \end{equation}$$
Note that at this point, we could almost compute both remaining equations offline. The only difficulty is that we don't know \(f_0\) nor \(f_{90}\) when we precompute those integrals. We will see below that we can incorporate these terms at runtime for equation \ref{iblDFV}, alas, this is not possible for equation \ref{iblLD} and we have to assume \(f_0 = f_{90} = 1\) (i.e.: the fresnel term always evaluates to 1).
We also have to deal with the visibility term of the brdf, in practice keeping it yields to slightly worst results compared to the ground truth, so we also set \(V = 1\).
Let's substitute \(f\) in equations \ref{iblLD} and \ref{iblDFV}:
$$\begin{equation} f(l_i, n, \alpha) = D(h_i, \alpha)F(f_0, f_{90}, \left<\VoH\right>)V(l_i, v, \alpha) \end{equation}$$
The first simplification is that the term \(D(h_i, \alpha)\) in the brdf cancels out with the denominator (which came from the \(pdf\) due to importance sampling) and F and V disappear since we assume their value is 1.
$$\begin{equation} LD(n, \alpha) \equiv \frac{\sum_i^N V(l_i, v, \alpha)\left<\NoL\right>\Lt(l_i) }{\sum_i^N \left<\NoL\right>} \end{equation}$$ $$\begin{equation}\label{iblFV} I(f(n, v, \alpha)) \equiv \frac{4}{N}\sum_i^N \color{green}{F(f_0, f_{90}, \left<\VoH\right>)} V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \end{equation}$$
Now, let's substitute the fresnel term into equation \ref{iblFV}:
$$\begin{equation} F(f_0, f_{90}, \left<\VoH\right>) = f_0 (1  F_c(\left<\VoH\right>)) + f_{90} F_c(\left<\VoH\right>) \\ F_c(\left<\VoH\right>) = (1  \left<\VoH\right>)^5 \end{equation}$$
$$\begin{equation} I(f(n, v, \alpha)) \equiv \frac{4}{N}\sum_i^N \left[\color{green}{f_0 (1  F_c(\left<\VoH\right>)) + f_{90} F_c(\left<\VoH\right>)}\right] V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \\ \end{equation}$$
$$ \begin{align*} I(f(n, v, \alpha)) \equiv & \color{green}{f_0 } \frac{4}{N}\sum_i^N \color{green}{(1  F_c(\left<\VoH\right>))} V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \\ + & \color{green}{f_{90}} \frac{4}{N}\sum_i^N \color{green}{ F_c(\left<\VoH\right>) } V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \end{align*} $$
And finally, we extract the equations that can be calculated offline (i.e.: the part that doesn't depend on the runtime parameters \(f_0\) and \(f_{90}\)):
$$\begin{equation}\label{iblAllEquations} DFG_1(\alpha, \left<\NoV\right>) = \frac{4}{N}\sum_i^N \color{green}{(1  F_c(\left<\VoH\right>))} V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \\ DFG_2(\alpha, \left<\NoV\right>) = \frac{4}{N}\sum_i^N \color{green}{ F_c(\left<\VoH\right>) } V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \\ I(f(n, v, \alpha)) \equiv \color{green}{f_0} \color{red}{DFG_1(\alpha, \left<\NoV\right>)} + \color{green}{f_{90}} \color{red}{DFG_2(\alpha, \left<\NoV\right>)} \end{equation}$$
Notice that \(DFG_1\) and \(DFG_2\) only depend on \(\NoV\), that is the angle between the normal \(n\) and the view direction \(v\). This is true because the integral is symmetrical with respect to \(n\). When integrating, we can choose any \(v\) we please as long as it satisfies \(\NoV\) (e.g.: when calculating \(\VoH\)).
Putting everything back together:
$$ \begin{align*} \Lout(n,v,\alpha,f_0,f_{90}) &\simeq \big[ f_0 \color{red}{DFG_1(\NoV, \alpha)} + f_{90} \color{red}{DFG_2(\NoV, \alpha)} \big] \times LD(n, \alpha) \\ DFG_1(\alpha, \left<\NoV\right>) &= \frac{4}{N}\sum_i^N \color{green}{(1  F_c(\left<\VoH\right>))} V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \\ DFG_2(\alpha, \left<\NoV\right>) &= \frac{4}{N}\sum_i^N \color{green}{ F_c(\left<\VoH\right>) } V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \\ LD(n, \alpha) &= \frac{\sum_i^N V(l_i, n, \alpha)\left<\NoL\right>\Lt(l_i) }{\sum_i^N \left<\NoL\right>} \end{align*} $$
Both \(DFG_1\) and \(DFG_2\) can either be precalculated in a regular 2D texture indexed by \((\NoV, \alpha)\) and sampled bilinearly, or computed at runtime using an analytic approximation of the surfaces. See sample code in the annex. The precalculated textures are shown in table 15. A C++ implementation of the precomputation can be found in section 9.5.
\(DFG_1\) and \(DFG_2\) are conveniently within the \([0, 1]\) range, however 8bits textures don't have enough precision and will cause problems. Unfortunately, on mobile, 16bits or float textures are not ubiquitous and there are a limited number of samplers. Despite the attractive simplicity of the shader code using a texture, it might be better to use an analytic approximation. Note however that since we only need to store two terms, OpenGL ES 3.0's RG16F texture format is a good candidate.
Such analytic approximation is described in [Karis14], itself based on [Lazarov13]. [Narkowicz14] is another interesting approximation. Note that these two approximations are not compatible with the energy compensation term presented in section 5.3.4.7. Table 16 presents a visual representation of these approximations.
\(LD\) is the convolution of the environment by a function that only depends on the \(\alpha\) parameter (itself related to the roughness, see section 4.8.3.3). \(LD\) can conveniently be stored in a mipmapped cubemap where increasing LODs receive the environment prefiltered with increasing roughness. This works well because this convolution is a powerful lowpass filter. To make good use of each mipmap level, it is necessary to remap \(\alpha\); we find that using a power remapping with \(\gamma = 2\) works well and is convenient.
$$ \begin{align*} \alpha &= perceptualRoughness^2 \\ lod_{\alpha} &= \alpha^{\frac{1}{2}} = perceptualRoughness \\ \end{align*} $$
See an example below:
Figure 53 shows how indirect lighting interacts with dielectrics and conductors. Direct lighting was removed for illustration purposes.
Listing 27 presents a GLSL implementation to evaluate the IBL, using the various textures described in the previous sections.
vec3 ibl(vec3 n, vec3 v, vec3 diffuseColor, vec3 f0, vec3 f90,
float perceptualRoughness) {
vec3 r = reflect(n);
vec3 Ld = textureCube(irradianceEnvMap, r) * diffuseColor;
float lod = computeLODFromRoughness(perceptualRoughness);
vec3 Lld = textureCube(prefilteredEnvMap, r, lod);
vec2 Ldfg = textureLod(dfgLut, vec2(dot(n, v), perceptualRoughness), 0.0).xy;
vec3 Lr = (f0 * Ldfg.x + f90 * Ldfg.y) * Lld;
return Ld + Lr;
}
We can however save a couple of texture lookups by using Spherical Harmonics instead of an irradiance cubemap and the analytical approximation of the \(DFG\) LUT, as shown in listing 28.
vec3 irradianceSH(vec3 n) {
// uniform vec3 sphericalHarmonics[9]
// We can use only the first 2 bands for better performance
return
sphericalHarmonics[0]
+ sphericalHarmonics[1] * (n.y)
+ sphericalHarmonics[2] * (n.z)
+ sphericalHarmonics[3] * (n.x)
+ sphericalHarmonics[4] * (n.y * n.x)
+ sphericalHarmonics[5] * (n.y * n.z)
+ sphericalHarmonics[6] * (3.0 * n.z * n.z  1.0)
+ sphericalHarmonics[7] * (n.z * n.x)
+ sphericalHarmonics[8] * (n.x * n.x  n.y * n.y);
}
// NOTE: this is the DFG LUT implementation of the function above
vec2 prefilteredDFG_LUT(float coord, float NoV) {
// coord = sqrt(roughness), which is the mapping used by the
// IBL prefiltering code when computing the mipmaps
return textureLod(dfgLut, vec2(NoV, coord), 0.0).rg;
}
vec3 evaluateSpecularIBL(vec3 r, float perceptualRoughness) {
// This assumes a 256x256 cubemap, with 9 mip levels
float lod = 8.0 * perceptualRoughness;
// decodeEnvironmentMap() either decodes RGBM or is a noop if the
// cubemap is stored in a float texture
return decodeEnvironmentMap(textureCubeLodEXT(environmentMap, r, lod));
}
vec3 evaluateIBL(vec3 n, vec3 v, vec3 diffuseColor, vec3 f0, vec3 f90, float perceptualRoughness) {
float NoV = max(dot(n, v), 0.0);
vec3 r = reflect(v, n);
// Specular indirect
vec3 indirectSpecular = evaluateSpecularIBL(r, perceptualRoughness);
vec2 env = prefilteredDFG_LUT(perceptualRoughness, NoV);
vec3 specularColor = f0 * env.x + f90 * env.y;
// Diffuse indirect
// We multiply by the Lambertian BRDF to compute radiance from irradiance
// With the Disney BRDF we would have to remove the Fresnel term that
// depends on NoL (it would be rolled into the SH). The Lambertian BRDF
// can be baked directly in the SH to save a multiplication here
vec3 indirectDiffuse = max(irradianceSH(n), 0.0) * Fd_Lambert();
// Indirect contribution
return diffuseColor * indirectDiffuse + indirectSpecular * specularColor;
}
In section 4.7.2 we discussed how to use a second scaled specular lobe to compensate for the energy loss due to only accounting for a single scattering event in our BRDF. This energy compensation lobe is scaled by a term that depends on \(r\) defined in the following way:
$$\begin{equation} r = \int_{\Omega} D(l,v) V(l,v) \left< \NoL \right> \partial l \end{equation}$$
Or, evaluated with importance sampling (See Importance Sampling For The IBL section):
$$\begin{equation} r \equiv \frac{4}{N}\sum_i^N V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \end{equation}$$
This equality is very similar to the terms \(DFG_1\) and \(DFG_2\) seen in equation \(\ref{iblAllEquations}\). In fact, it's the same, except without the Fresnel term.
By making the further assumption that \(f_{90} = 1\), we can rewrite \(DFG_1\) and \(DFG_2\) and the \(\Lout\) reconstruction:
$$ \begin{align*} \Lout(n,v,\alpha,f_0) &\simeq \big[ (1  f_0) \color{red}{DFG_1^{multiscatter}(\NoV, \alpha)} + f_0 \color{red}{DFG_2^{multiscatter}(\NoV, \alpha)} \big] \times LD(n, \alpha) \\ DFG_1^{multiscatter}(\alpha, \left<\NoV\right>) &= \frac{4}{N}\sum_i^N \color{green}{F_c(\left<\VoH\right>)} V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \\ DFG_2^{multiscatter}(\alpha, \left<\NoV\right>) &= \frac{4}{N}\sum_i^N V(l_i, v, \alpha)\frac{\left<\VoH\right>}{\left<\NoH\right>} \left<\NoL\right> \\ LD(n, \alpha) &= \frac{\sum_i^N V(l_i, n, \alpha)\left<\NoL\right>\Lt(l_i) }{\sum_i^N V(l_i, n, \alpha)\left<\NoL\right>} \end{align*} $$
These two new \(DFG\) terms simply need to replace the ones used in the implementation shown in section 9.5:
float Fc = pow(1  VoH, 5.0f);
r.x += Gv * Fc;
r.y += Gv;
To perform the reconstruction we need to slightly modify listing 30:
vec2 dfg = textureLod(dfgLut, vec2(dot(n, v), perceptualRoughness), 0.0).xy;
// (1  f0) * dfg.x + f0 * dfg.y
vec3 specularColor = mix(dfg.xxx, dfg.yyy, f0);
In order to calculate the specular contribution of distant imagebased lights, we had to make a few approximations and compromises:
When sampling the IBL, the clear coat layer is calculated as a second specular lobe. This specular lobe is oriented along the view direction since we cannot reasonably integrate over the hemisphere. Listing 31 demonstrates this approximation in practice. It also shows the energy conservation step. It is important to note that this second specular lobe is computed exactly the same way as the main specular lobe, using the same DFG approximation.
// clearCoat_NoV == shading_NoV if the clear coat layer doesn't have its own normal map
float Fc = F_Schlick(0.04, 1.0, clearCoat_NoV) * clearCoat;
// base layer attenuation for energy compensation
iblDiffuse *= 1.0  Fc;
iblSpecular *= sq(1.0  Fc);
iblSpecular += specularIBL(r, clearCoatPerceptualRoughness) * Fc;
[McAuley15] describes a technique called “bent reflection vector”, based [Revie12]. The bent reflection vector is a rough approximation of anisotropic lighting but the alternative is to use importance sampling. This approximation is sufficiently cheap to compute and provides good results, as shown in figure 59 and figure 60.
The implementation of this technique is straightforward, as demonstrated in listing 32.
vec3 anisotropicTangent = cross(bitangent, v);
vec3 anisotropicNormal = cross(anisotropicTangent, bitangent);
vec3 bentNormal = normalize(mix(n, anisotropicNormal, anisotropy));
vec3 r = reflect(v, bentNormal);
This technique can be made more useful by accepting negative anisotropy
values, as shown in listing 33. When the anisotropy is negative, the highlights are not in the direction of the tangent, but in the direction of the bitangent instead.
vec3 anisotropicDirection = anisotropy >= 0.0 ? bitangent : tangent;
vec3 anisotropicTangent = cross(anisotropicDirection, v);
vec3 anisotropicNormal = cross(anisotropicTangent, anisotropicDirection);
vec3 bentNormal = normalize(mix(n, anisotropicNormal, anisotropy));
vec3 r = reflect(v, bentNormal);
Figure 61 demonstrates this modified implementation in practice.
[TODO] Explain subsurface and IBL
The IBL implementation for the cloth material model is more complicated than for the other material models. The main difference stems from the use of a different NDF (“Charlie” vs heightcorrelated Smith GGX). As described in this section, we use the splitsum approximation to compute the DFG term of the BRDF when computing an IBL. This DFG term is designed for a different BRDF and cannot be used for the cloth BRDF. Since we designed our cloth BRDF to not need a Fresnel term, we can generate a single DG term in the 3rd channel of the DFG LUT. The result is shown in figure 62.
The DG term is generated using uniform sampling as recommended in [Estevez17]. With uniform sampling the \(pdf\) is simply \(\frac{1}{2\pi}\) and we must still use the Jacobian \(\frac{1}{4\left< \VoH \right>}\).
The remainder of the imagebased lighting implementation follows the same steps as the implementation of regular lights, including the optional subsurface scattering term and its wrap diffuse component. Just as with the clear coat IBL implementation, we cannot integrate over the hemisphere and use the view direction as the dominant light direction to compute the wrap diffuse component.
float diffuse = Fd_Lambert() * ambientOcclusion;
#if defined(SHADING_MODEL_CLOTH)
#if defined(MATERIAL_HAS_SUBSURFACE_COLOR)
diffuse *= saturate((NoV + 0.5) / 2.25);
#endif
#endif
vec3 indirectDiffuse = irradianceIBL(n) * diffuse;
#if defined(SHADING_MODEL_CLOTH) && defined(MATERIAL_HAS_SUBSURFACE_COLOR)
indirectDiffuse *= saturate(subsurfaceColor + NoV);
#endif
vec3 ibl = diffuseColor * indirectDiffuse + indirectSpecular * specularColor;
It is important to note that this only addresses part of the IBL problem. The prefiltered specular environment maps described earlier are convolved with the standard shading model's BRDF, which differs from the cloth BRDF. To get accurate result we should in theory provide one set of IBLs per BRDF used in the engine. Providing a second set of IBLs is however not practical for our use case so we decided to rely on the existing IBLs instead.
[TODO] Sphericalharmonics or sphericalgaussian lightmaps, irradiance volumes, PRT?…
Transparent and translucent materials are important to add realism and correctness to scenes. Filament must therefore provide lighting models for both types of materials to allow artists to properly recreate realistic scenes. Translucency can also be used effectively in a number of nonrealistic settings.
To properly light a transparent surface, we must first understand how the material's opacity is applied. Observe a window and you will see that the diffuse reflectance is transparent. On the other hand, the brighter the specular reflectance, the less opaque the window appears. This effect can be seen in figure 63: the scene is properly reflected onto the glass surfaces but the specular highlight of the sun is bright enough to appear opaque.
To properly implement opacity, we will use the premultiplied alpha format. Given a desired opacity noted \( \alpha_{opacity} \) and a diffuse color \( \sigma \) (linear, unpremultiplied), we can compute the effective opacity of a fragment.
$$\begin{align*} color &= \sigma * \alpha_{opacity} \\ opacity &= \alpha_{opacity} \end{align*}$$
The physical interpretation is that the RGB components of the source color define how much light is emitted by the pixel, whereas the alpha component defines how much of the light behind the pixel is blocked by said pixel. We must therefore use the following blending functions:
$$\begin{align*} Blend_{src} &= 1 \\ Blend_{dst} &= 1  src_{\alpha} \end{align*}$$
The GLSL implementation of these equations is presented in listing 35.
// baseColor has already been premultiplied
vec4 shadeSurface(vec4 baseColor) {
float alpha = baseColor.a;
vec3 diffuseColor = evaluateDiffuseLighting();
vec3 specularColor = evaluateSpecularLighting();
return vec4(diffuseColor + specularColor, alpha);
}
Translucent materials can be divided into two categories:
Volume translucency is useful to light particle systems, for instance clouds or smoke. Surface translucency can be used to imitate materials with transmitted scattering such as wax, marble, skin, etc.
[TODO] Surface translucency (BRDF+BTDF, BSSRDF)
Occlusion is an important darkening factor used to recreate shadowing at various scales:
Microocclusion used to handle creases, cracks and cavities.  
Macroocclusion used to handle occlusion by an object's own geometry or by geometry baked in normal maps (bricks, etc.).  
Occlusion coming from contact between objects, or from an object's own geometry. 
Medium scale ambient occlusion is prebaked in ambient occlusion maps, exposed as a material parameter, as seen in the material parameterization section earlier.
Large scale ambient occlusion is often computed using screenspace techniques such as SSAO (screenspace ambient occlusion), HBAO (horizon based ambient occlusion), etc. Note that these techniques can also contribute to medium scale ambient occlusion when the camera is close enough to surfaces.
Note: to prevent over darkening when using both medium and large scale occlusion, Lagarde recommends to use \(min({AO}_{medium}, {AO}_{large})\).
Morgan McGuire formalizes ambient occlusion in the context of physically based rendering in [McGuire10]. In his formulation, McGuire defines an ambient illumination function \( L_a \), which in our case is encoded with spherical harmonics. He also defines a visibility function \(V\), with \(V(l)=1\) if there is an unoccluded line of sight from the surface in direction \(l\), and 0 otherwise.
With these two functions, the ambient term of the rendering equation can be expressed as shown in equation \(\ref{diffuseAO}\).
$$\begin{equation}\label{diffuseAO} L(l,v) = \int_{\Omega} f(l,v) L_a(l) V(l) \left< \NoL \right> dl \end{equation}$$
This expression can be approximated by separating the visibility term from the illumination function, as shown in equation \(\ref{diffuseAOApprox}\).
$$\begin{equation}\label{diffuseAOApprox} L(l,v) \approx \left( \pi \int_{\Omega} f(l,v) L_a(l) dl \right) \left( \frac{1}{\pi} \int_{\Omega} V(l) \left< \NoL \right> dl \right) \end{equation}$$
This approximation is only exact when the distant light \( L_a \) is constant and \(f\) is a Lambertian term. McGuire states however that this approximation is reasonable if both functions are relatively smooth over most of the sphere. This happens to be the case with a distant light probe (IBL).
The left term of this approximation is the precomputed diffuse component of our IBL. The right term is a scalar factor between 0 and 1 that indicates the fractional accessibility of a point. Its opposite is the diffuse ambient occlusion term, show in equation \(\ref{diffuseAOTerm}\).
$$\begin{equation}\label{diffuseAOTerm} {AO} = 1  \frac{1}{\pi} \int_{\Omega} V(l) \left< \NoL \right> dl \end{equation}$$
Since we use a precomputed diffuse term, we cannot compute the exact accessibility of shaded points at runtime. To compensate for this lack of information in our precomputed term, we partially reconstruct incident lighting by applying an ambient occlusion factor specific to the surface's material at the shaded point.
In practice, baked ambient occlusion is stored as a grayscale texture which can often be lower resolution than other textures (base color or normals for instance). It is important to note that the ambient occlusion property of our material model intends to recreate macrolevel diffuse ambient occlusion. While this approximation is not physically correct, it constitutes an acceptable tradeoff of quality vs performance.
Figure 66 shows two different materials without and with diffuse ambient occlusion. Notice how the material ambient occlusion is used to recreate the natural shadowing that occurs between the different tiles. Without ambient occlusion, both materials appear too flat.
Applying baked diffuse ambient occlusion in a GLSL shader is straightforward, as shown in listing 36.
// diffuse indirect
vec3 indirectDiffuse = max(irradianceSH(n), 0.0) * Fd_Lambert();
// ambient occlusion
indirectDiffuse *= texture2D(aoMap, outUV).r;
Note how the ambient occlusion term is only applied to indirect lighting.
Specular microocclusion can be derived from \(\fNormal\), itself derived from the diffuse color. The derivation is based on the knowledge that no realworld material has a reflectance lower than 2%. Values in the 02% range can therefore be treated as prebaked specular occlusion used to smoothly extinguish the Fresnel term.
float f90 = clamp(dot(f0, 50.0 * 0.33), 0.0, 1.0);
// cheap luminance approximation
float f90 = clamp(50.0 * f0.g, 0.0, 1.0);
The derivations mentioned earlier for ambient occlusion assume Lambertian surfaces and are only valid for indirect diffuse lighting. The lack of information about surface accessibility is particularly harmful to the reconstruction of indirect specular lighting. It usually manifests itself as light leaks.
Sébastien Lagarde proposes an empirical approach to derive the specular occlusion term from the diffuse occlusion term in [Lagarde14]. The result does not have any physical basis but produces visually pleasant results. The goal of his formulation is return the diffuse occlusion term unmodified for rough surfaces. For smooth surfaces, the formulation, implemented in listing 38, reduces the influence of occlusion at normal incidence and increases it at grazing angles.
float computeSpecularAO(float NoV, float ao, float roughness) {
return clamp(pow(NoV + ao, exp2(16.0 * roughness  1.0))  1.0 + ao, 0.0, 1.0);
}
// specular indirect
vec3 indirectSpecular = evaluateSpecularIBL(r, perceptualRoughness);
// ambient occlusion
float ao = texture2D(aoMap, outUV).r;
indirectSpecular *= computeSpecularAO(NoV, ao, roughness);
Note how the specular occlusion factor is only applied to indirect lighting.
When computing the specular IBL contribution for a surface that uses a normal map, it is possible to end up with a reflection vector pointing towards the surface. If this reflection vector is used for shading directly, the surface will be lit in places where it should not be lit (assuming opaque surfaces). This is another occurrence of light leaking that can easily be minimized using a simple technique described by Jeff Russell [Russell15].
The key idea is to occlude light coming from behind the surface. This can easily be achieved since a negative dot product between the reflected vector and the surface's normal indicates a reflection vector pointing towards the surface. Our implementation shown in listing 39 is similar to Russell's, albeit without the artist controlled horizon fading factor.
// specular indirect
vec3 indirectSpecular = evaluateSpecularIBL(r, perceptualRoughness);
// horizon occlusion with falloff, should be computed for direct specular too
float horizon = min(1.0 + dot(r, n), 1.0);
indirectSpecular *= horizon * horizon;
Horizon specular occlusion fading is cheap but can easily be omitted to improve performance as needed.
There are two common use cases of normal maps: replacing highpoly meshes with lowpoly meshes (using a base map) and adding surface details (using a detail map).
Let's imagine that we want to render a piece of furniture covered in tufted leather. Modeling the geometry to accurately represent the tufted pattern would require too many triangles so we instead bake a highpoly mesh into a normal map. Once the base map is applied to a simplified mesh (in this case, a quad), we get the result in figure 67. The base map used to create this effect is shown in figure 68.
A simple problem arises if we now want to combine this base map with a second normal map. For instance, let's use the detail map shown in figure 69 to add cracks in the leather.
Given the nature of normal maps (XYZ components stored in tangent space), it is fairly obvious that naive approaches such as linear or overlay blending cannot work. We will use two more advanced techniques: a mathematically correct one and an approximation suitable for realtime shading.
Colin BarréBrisebois and Stephen Hill propose in [Hill12] a mathematically sound solution called Reoriented Normal Mapping, which consists in rotating the basis of the detail map onto the normal from the base map. This technique relies on the shortest arc quaternion to apply the rotation, which greatly simplifies thanks to the properties of the tangent space.
Following the simplifications described in [Hill12], we can produce the GLSL implementation shown in listing 40.
vec3 t = texture(baseMap, uv).xyz * vec3( 2.0, 2.0, 2.0) + vec3(1.0, 1.0, 0.0);
vec3 u = texture(detailMap, uv).xyz * vec3(2.0, 2.0, 2.0) + vec3( 1.0, 1.0, 1.0);
vec3 r = normalize(t * dot(t, u)  u * t.z);
return r;
Note that this implementation assumes that the normals are stored uncompressed and in the [0..1] range in the source textures.
The normalization step is not strictly necessary and can be skipped if the technique is used at runtime. If so, the computation of r
becomes t * dot(t, u) / t.z  u
.
Since this technique is slightly more expensive than the one described below, we will mostly use it offline. We therefore provide a simple offline tool to combine two normal maps. Figure 70 presents the output of the tool with the base map and the detail map shown previously.
The technique called UDN blending, described in [Hill12], is a variant of the partial derivative blending technique. Its main advantage is the low number of shader instructions it requires (see listing 41). While it leads to a reduction in details over flat areas, UDN blending is interesting if blending must be performed at runtime.
vec3 t = texture(baseMap, uv).xyz * 2.0  1.0;
vec3 u = texture(detailMap, uv).xyz * 2.0  1.0;
vec3 r = normalize(t.xy + u.xy, t.z);
return r;
The results are visually close to Reoriented Normal Mapping but a careful comparison of the data shows that UDN is indeed less correct. Figure 71 presents the result of the UDN blending approach using the same source data as in the previous examples.
[TODO] MSAA, geometric AA (normals and roughness), shader antialiasing (objectspace shading?)
The lighting section of this document describes how light interacts with surfaces in the scene in a physically based manner. To achieve plausible results, we must go a step further and consider the transformations necessary to convert the scene luminance, as computed by our lighting equations, into displayable pixel values.
The series of transformations we are going to use form the following imaging pipeline:
Note: the OETF step is the application of the optoelectronic transfer function of the target color space. For clarity this diagram does not include postprocessing steps such as vignette, bloom, etc. These effects will be discussed separately.
[TODO] Color spaces (ACES, sRGB, Rec. 709, Rec. 2020, etc.), gamma/linear, etc.
The first step in the image transformation process is to use a physically based camera to properly expose the scene's outgoing luminance.
Because we use photometric units throughout the lighting pipeline, the light reaching the camera is an energy expressed in luminance \(L\), in \(cd.m^{2}\). Light incident to the camera sensor can cover a large range of values, from \(10^{5}cd.m^{2}\) for starlight to \(10^{9}cd.m^{2}\) for the sun. Since we obviously cannot manipulate and even less record such a large range of values, we need to remap them.
This range remapping is done in a camera by exposing the sensor for a certain time. To maximize the use of the limited range of the sensor, the scene's light range is centered around the “middle gray”, a value halfway between black and white. The exposition is therefore achieved by manipulating, either manually or automatically, 3 settings:
Noted \(N\) and expressed in fstops ƒ, this setting controls how open or closed the camera system's aperture is. Since an fstop indicate the ratio of the lens' focal length to the diameter of the entrance pupil, highvalues (ƒ/16) indicate a small aperture and small values (ƒ/1.4) indicate a wide aperture. In addition to the exposition, the aperture setting controls the depth of field.
Noted \(t\) and expressed in seconds \(s\), this setting controls how long the aperture remains opened (it also controls the timing of the sensor shutter(s), whether electronic or mechanical). In addition to the exposition, the shutter speed controls motion blur.
Noted \(S\) and expressed in ISO, this setting controls how the light reaching the sensor is quantized. Because of its unit, this setting is often referred to as simply the “ISO” or “ISO setting”. In addition to the exposition, the sensitivity setting controls the amount of noise.
Since referring to these 3 settings in our equations would be unwieldy, we instead summarize the “exposure triangle” by an exposure value, noted EV^{10}.
The EV is expressed in a base2 logarithmic scale, with a difference of 1 EV called a stop. One positive stop (+1 EV) corresponds to a factor of two in luminance and one negative stop (−1 EV) corresponds to a factor of half in luminance.
Equation \( \ref{ev} \) shows the formal definition of EV.
$$\begin{equation}\label{ev} EV = log_2(\frac{N^2}{t}) \end{equation}$$
Note that this definition is only function of the aperture and shutter speed, but not the sensitivity. An exposure value is by convention defined for ISO 100, or \( EV_{100} \), and because we wish to work with this convention, we need to be able to express \( EV_{100} \) as a function of the sensitivity.
Since we know that EV is a base2 logarithmic scale in which each stop increases or decreases the brightness by a factor of 2, we can formally define \( EV_{S} \), the exposure value at given sensitivity (equation \(\ref{evS}\)).
$$\begin{equation}\label{evS} {EV}_S = EV_{100} + log_2(\frac{S}{100}) \end{equation}$$
Calculating the \( EV_{100} \) as a function of the 3 camera settings is trivial, as shown in \(\ref{ev100}\).
$$\begin{equation}\label{ev100} {EV}_{100} = EV_{S}  log_2(\frac{S}{100}) = log_2(\frac{N^2}{t})  log_2(\frac{S}{100}) \end{equation}$$
Note that the operator (photographer, etc.) can achieve the same exposure (and therefore EV) with several combinations of aperture, shutter speed and sensitivity. This allows some artistic control in the process (depth of field vs motion blur vs grain).
A camera, similar to a spot meter, is able to measure the average luminance of a scene and convert it into EV to achieve automatic exposure, or at the very least offer the user exposure guidance.
It is possible to define EV as a function of the scene luminance \(L\), given a perdevice calibration constant \(K\) (equation \( \ref{evK} \)).
$$\begin{equation}\label{evK} EV = log_2(\frac{L \times S}{K}) \end{equation}$$
That constant \(K\) is the reflectedlight meter constant, which varies between manufacturers. We could find two common values for this constant: 12.5, used by Canon, Nikon and Sekonic, and 14, used by Pentax and Minolta. Given the wide availability of Canon and Nikon cameras, as well as our own usage of Sekonic light meters, we will choose to use \( K = 12.5 \).
Since we want to work with \( EV_{100} \), we can substitute \(K\) and \(S\) in equation \( \ref{evK} \) to obtain equation \( \ref{ev100L} \).
$$\begin{equation}\label{ev100L} EV = log_2(L \frac{100}{12.5}) \end{equation}$$
Given this relationship, it would be possible to implement automatic exposure in our engine by first measuring the average luminance of a frame. An easy way to achieve this is to simply downsample a luminance buffer down to 1 pixel and read the remaining value. This technique is unfortunately rarely stable and can easily be affected by extreme values. Many games use a different approach which consists in using a luminance histogram to remove extreme values.
For validation and testing purposes, the luminance can be computed from a given EV:
$$\begin{equation} L = 2^{EV_{100}} \times \frac{12.5}{100} = 2^{EV_{100}  3} \end{equation}$$
It is possible to define EV as a function of the illuminance \(E\), given a perdevice calibration constant \(C\):
$$\begin{equation}\label{evC} EV = log_2(\frac{E \times S}{C}) \end{equation}$$
The constant \(C\) is the incidentlight meter constant, which varies between manufacturers and/or types of sensors. There are two common types of sensors: flat and hemispherical. For flat sensors, a common value is 250. With hemispherical sensors, we could find two common values: 320, used by Minolta, and 340, used by Sekonic.
Since we want to work with \( EV_{100} \), we can substitute \(S\) \( \ref{evC} \) to obtain equation \( \ref{ev100C} \).
$$\begin{equation}\label{ev100C} EV = log_2(E \frac{100}{C}) \end{equation}$$
The illuminance can then be computed from a given EV. For a flat sensor with \( C = 250 \) we obtain equation \( \ref{eFlatSensor} \).
$$\begin{equation}\label{eFlatSensor} E = 2^{EV_{100}} \times 2.5 \end{equation}$$
For a hemispherical sensor with \( C = 340 \) we obtain equation \( \ref{eHemisphereSensor} \)
$$\begin{equation}\label{eHemisphereSensor} E = 2^{EV_{100}} \times 3.4 \end{equation}$$
Even though an exposure value actually indicates combinations of camera settings, it is often used by photographers to describe light intensity. This is why cameras let photographers apply an exposure compensation to over or underexpose an image. This setting can be used for artistic control but also to achieve proper exposure (snow for instance will be exposed for as 18% middlegray).
Applying an exposure compensation \(EC\) is a simple as adding an offset to the exposure value, as shown in equation \( \ref{ec} \).
$$\begin{equation}\label{ec} EV_{100}' = EV_{100}  EC \end{equation}$$
This equation uses a negative sign because we are using \(EC\) in fstops to adjust the final exposure. Increasing the EV is akin to closing down the aperture of the lens (or reducing shutter speed or reducing sensitivity). A higher EV will produce darker images.
To convert the scene luminance into normalized luminance, we must use the photometric exposure (or luminous exposure), or amount of scene luminance that reaches the camera sensor. The photometric exposure, expressed in lux seconds and noted \(H\), is given by equation \( \ref{photometricExposure} \).
$$\begin{equation}\label{photometricExposure} H = \frac{q \cdot t}{N^2} L \end{equation}$$
Where \(L\) is the luminance of the scene, \(t\) the shutter speed, \(N\) the aperture and \(q\) the lens and vignetting attenuation (typically \( q = 0.65 \)^{11}). This definition does not take the sensor sensitivity into account. To do so, we must use one of the three ways to relate photometric exposure and sensitivity: saturationbased speed, noisebased speed and standard output sensitivity.
We choose the saturationbased speed relation, which gives us \( H_{sat} \), the maximum possible exposure that does not lead to clipped or bloomed camera output (equation \( \ref{hSat} \)).
$$\begin{equation}\label{hSat} H_{sat} = \frac{78}{S_{sat}} \end{equation}$$
We combine equations \( \ref{hSat} \) and \( \ref{photometricExposure} \) in equation \( \ref{lmax} \) to compute the maximum luminance \( L_{max} \) that will saturate the sensor given exposure settings \(S\), \(N\) and \(t\).
$$\begin{equation}\label{lmax} L_{max} = \frac{N^2}{q \cdot t} \frac{78}{S} \end{equation}$$
This maximum luminance can then be used to normalize incident luminance \(L\) as shown in equation \( \ref{normalizedLuminance} \).
$$\begin{equation}\label{normalizedLuminance} L' = L \frac{1}{L_{max}} \end{equation}$$
\( L_{max} \) can be simplified using equation \( \ref{ev} \), \( S = 100 \) and \( q = 0.65 \):
$$\begin{align*} L_{max} &= \frac{N^2}{t} \frac{78}{q \cdot S} \\ L_{max} &= 2^{EV_{100}} \frac{78}{q \cdot S} \\ L_{max} &= 2^{EV_{100}} \times 1.2 \end{align*}$$
Listing 42 shows how the exposure term can be applied directly to the pixel color computed in a fragment shader.
// Computes the camera's EV100 from exposure settings
// aperture in fstops
// shutterSpeed in seconds
// sensitivity in ISO
float exposureSettings(float aperture, float shutterSpeed, float sensitivity) {
return log2((aperture * aperture) / shutterSpeed * 100.0 / sensitivity);
}
// Computes the exposure normalization factor from
// the camera's EV100
float exposure(ev100) {
return 1.0 / (pow(2.0, ev100) * 1.2);
}
float ev100 = exposureSettings(aperture, shutterSpeed, sensitivity);
float exposure = exposure(ev100);
vec4 color = evaluateLighting();
color.rgb *= exposure;
In practice the exposure factor can be precomputed on the CPU to save shader instructions.
The process described above relies on artists setting the camera exposure settings manually. This can prove cumbersome in practice since camera movements and/or dynamic effects can greatly affect the scene's luminance. Since we know how to compute the exposure value from a given luminance (see section 8.1.2.1), we can transform our camera into a spot meter. To do so, we need to measure the scene's luminance.
There are two common techniques used to measure the scene's luminance:
Note that both methods will find the average luminance after multiplication by the albedo. This is not entirely correct but the alternative is to keep a luminance buffer that contains the luminance of each pixel before multiplication by the surface albedo. This is expensive both computationally and memorywise.
These two techniques also limit the metering system to average metering, where each pixel has the same influence (or weight) over the final exposure. Cameras typically offer 3 modes of metering:
In which only a small circle in the center of the image contributes to the final exposure. That circle is usually 1 to 5% of the total image size.
Gives more influence to scene luminance values located in the center of the screen.
A metering mode that differs for each manufacturer. The goal of this mode is to prioritize exposure for the most important parts of the scene. This is often achieved by splitting the image into a grid and by classifying each cell (using focus information, min/max luminance, etc.). Advanced implementations attempt to compare the scene to a known dataset to achieve proper exposure (backlit sunset, overcast snowy day, etc.).
The weight \(w\) of each luminance value to use when computing the scene luminance is given by equation \( \ref{spotMetering} \).
$$\begin{equation}\label{spotMetering} w(x,y) = \begin{cases} 1 & \left p_{x,y}  s_{x,y} \right \le s_r \\ 0 & \left p_{x,y}  s_{x,y} \right \gt s_r \end{cases} \end{equation}$$
Where \(p\) is the position of the pixel, \(s\) the center of the spot and \( s_r \) the radius of the spot.
$$\begin{equation}\label{centerMetering} w(x,y) = smooth(\left p_{x,y}  c \right \times \frac{2}{width} ) \end{equation}$$
Where \(c\) is the center of the time and \( smooth() \) a smoothing function such as GLSL's smoothstep()
.
To smooth the result of the metering, we can use equation \( \ref{adaptation} \), an exponential feedback loop as described by Pattanaik et al. in [Pattanaik00].
$$\begin{equation}\label{adaptation} L_{avg} = L_{avg} + (L  L_{avg}) \times (1  e^{\Delta t \cdot \tau}) \end{equation}$$
Where \( \Delta t \) is the delta time from the previous frame and \(\tau\) a constant that controls the adaptation rate.
Because the EV scale is almost perceptually linear, the exposure value is also often used as a light unit. This means we could let artists specify the intensity of lights or emissive surfaces using exposure compensation as a unit. The intensity of emitted light would therefore be relative to the exposure settings. Using exposure compensation as a light unit should be avoided whenever possible but can be useful to force (or cancel) a bloom effect around emissive surfaces independently of the camera settings (for instance, a lightsaber in a game should always bloom).
With \(c\) the bloom color and \( EV_{100} \) the current exposure value, we can easily compute the luminance of the bloom value as show in equation \( \ref{bloomEV} \).
$$\begin{equation}\label{bloomEV} EV_{bloom} = EV_{100} + EC \\ L_{bloom} = c \times 2^{EV_{bloom}  3} \end{equation}$$
Equation \( \ref{bloomEV} \) can be used in a fragment shader to implement emissive blooms, as shown in listing 43.
vec4 surfaceShading() {
vec4 color = evaluateLights();
// rgb = color, w = exposure compensation
vec4 emissive = getEmissive();
color.rgb += emissive.rgb * pow(2.0, ev100 + emissive.w  3.0);
color.rgb *= exposure;
return color;
}
[TODO]
[TODO] Notes: there is a physically based approach to generating lens flares, by tracing rays through the optical assembly of the lens, but we are going to use an imagebased approach. This approach is cheaper and has a few welcome benefits such as free emitters occlusion and unlimited light sources support.
[TODO] Perform postprocessing on the scene referred data (linear space, before tonemapping) as much as possible
It is important to provide color correction tools to give artists greater artistic control over the final image. These tools are found in every photo or video processing application, such as Adobe Photoshop or Adobe After Effects.
The light path, or rendering method, used by the engine can have serious performance implications and may impose strong limitations on how many lights can be used in a scene. There are traditionally two different rendering methods used by 3D engines forward and deferred rendering.
Our goal is to use a rendering method that obeys the following constraints:
Additionally, we would like to easily support:
Deferred rendering is used by many modern 3D rendering engines to easily support dozens, hundreds or even thousands of light source (amongst other benefits). This method is unfortunately very expensive in terms of bandwidth. With our default PBR material model, our Gbuffer would use between 160 and 192 bits per pixel, which would translate directly to rather high bandwidth requirements.
Forward rendering methods on the other hand have historically been bad at handling multiple lights. A common implementation is to render the scene multiple times, once per visible light, and to blend (add) the results. Another technique consists in assigning a fixed maximum of lights to each object in the scene. This is however impractical when objects occupy a vast amount of space in the world (building, road, etc.).
Tiled shading can be applied to both forward and deferred rendering methods. The idea is to split the screen in a grid of tiles and for each tile, find the list of lights that affect the pixels within that tile. This has the advantage of reducing overdraw (in deferred rendering) and shading computations of large objects (in forward rendering). This technique suffers however from depth discontinuities issues that can lead to large amounts of extraneous work.
The scene displayed in figure 76 was rendered using clustered forward rendering.
Figure 77 shows the same scene split in tiles (in this case, a 1280×720 render target with 80×80px tiles).
We decided to explore another method called Clustered Shading, in its forward variant. Clustered shading expands on the idea of tiled rendering but adds a segmentation on the 3rd axis. The “clustering” is done in view space, by splitting the frustum into a 3D grid.
The frustum is first sliced on the depth axis as show in figure 78.
And the depth slices are then combined with the screen tiles to “voxelize” the frustum. We call each cluster a froxel as it makes it clear what they represent (a voxel in frustum space). The result of the “froxelization” pass is shown in figure 79 and figure 80.
Before rendering a frame, each light in the scene is assigned to any froxel it intersects with. The result of the lights assignment pass is a list of lights for each froxel. During the rendering pass, we can compute the ID of the froxel a fragment belongs to and therefore the list of lights that can affect that fragment.
The depth slicing is not linear, but exponential. In a typical scene, there will be more pixels close to the near plane than to the far plane. An exponential grid of froxels will therefore improve the assignment of lights where it matters the most.
Figure 81 shows how much world space unit each depth slice uses with exponential slicing.
A simple exponential voxelization is unfortunately not enough. The graphic above clearly illustrates how world space is distributed across slices but it fails to show what happens close to the near plane. If we examine the same distribution in a smaller range (0.1m to 7m) we can see an interesting problem appear as shown in figure 82.
This graphic shows that a simple exponential distribution uses up half of the slices very close to the camera. In this particular case, we use 8 slices out of 16in the first 5 meters. Since dynamic world lights are either point lights (spheres) or spot lights (cones), such a fine resolution is completely unnecessary so close to the near plane.
Our solution is to manually tweak the size of the first froxel depending on the scene and the near and far planes. By doing so, we can better distribute the remaining froxels across the frustum. Figure 83 shows for instance what happens when we use a special froxel between 0.1m and 5m.
This new distribution is much more efficient and allows a better assignment of the lights throughout the entire frustum.
Lights assignment can be done in two different ways, on the GPU or on the CPU.
This implementation requires OpenGL ES 3.1 and support for compute shaders. The lights are stored in Shader Storage Buffer Objects (SSBO) and passed to a compute shader that assigns each light to the corresponding froxels.
The frustum voxelization can be executed only once by a first compute shader (as long as the projection matrix does not change), and the lights assignment can be performed each frame by another compute shader.
The threading model of compute shaders is particularly well suited for this task. We simply invoke as many workgroups as we have froxels (we can directly map the X, Y and Z workgroup counts to our froxel grid resolution). Each workground will in turn be threaded and traverse all the lights to assign.
Intersection tests imply simple sphere/frustum or cone/frustum tests.
See the annex for the source code of a GPU implementation (point lights only).
On nonOpenGL ES 3.1 devices, lights assignment can be performed efficiently on the CPU. The algorithm is different from the GPU implementation. Instead of iterating over every light for each froxel, the engine will “rasterize” each light as froxels. For instance, given a point light’s center and radius, it is trivial to compute the list of froxels it intersects with.
This technique has the added benefit of providing tighter culling than in the GPU variant. The CPU implementation can also more easily generate a packed list of lights.
The list of lights per froxel can be passed to the fragment shader either as an SSBO (OpenGL ES 3.1) or a texture.
Given a near plane \(n\), a far plane \(f\), a maximum number of depth slices \(m\) and a linear depth value \(z\) in the range [0..1], equation \(\ref{zToCluster}\) can be used to compute the index of the cluster for a given position.
$$\begin{equation}\label{zToCluster} zToCluster(z,n,f,m)=floor \left( max \left( log2(z) \frac{m}{log2(\frac{n}{f})} + m, 0 \right) \right) \end{equation}$$
This formula suffers however from the resolution issue mentioned previously. We can fix it by introducing \(sn\), a special near value that defines the extent of the first froxel (the first froxel occupies the range [n..sn], the remaining froxels [sn..f]).
$$\begin{equation}\label{zToClusterFix} zToCluster(z,n,sn,f,m)=floor \left( max \left( log2(z) \frac{m1}{log2(\frac{sn}{f})} + m, 0 \right) \right) \end{equation}$$
Equation \(\ref{linearZ}\) can be used to compute a linear depth value from gl_FragCoord.z
(assuming a standard OpenGL projection matrix).
$$\begin{equation}\label{linearZ} linearZ(z)=\frac{n}{f+z(nf)} \end{equation}$$
This equation can be simplified by precomputing two terms \(c0\) and \(c1\), as shown in equation \(\ref{linearZFix}\).
$$\begin{equation}\label{linearZFix} c1 = \frac{f}{n} \\ c0 = 1  c1 \\ linearZ(z)=\frac{1}{z \cdot c0 + c1} \end{equation}$$
This simplification is important because we pass the linear z value to a log2
in \(\ref{zToClusterFix}\). Since the division becomes a negation under a logarithmic, we can avoid a division by using \(log2(z \cdot c0 + c1)\) instead.
All put together, computing the froxel index of a given fragment can be implemented fairly easily as shown in listing 44.
#define MAX_LIGHT_COUNT 16 // max number of lights per froxel
uniform uvec4 froxels; // res x, res y, count y, count y
uniform vec4 zParams; // c0, c1, index scale, index bias
uint getDepthSlice() {
return uint(max(0.0, log2(zParams.x * gl_FragCoord.z + zParams.y) *
zParams.z + zParams.w));
}
uint getFroxelOffset(uint depthSlice) {
uvec2 froxelCoord = uvec2(gl_FragCoord.xy) / froxels.xy;
froxelCoord.y = (froxels.w  1u)  froxelCoord.y;
uint index = froxelCoord.x + froxelCoord.y * froxels.z +
depthSlice * froxels.z * froxels.w;
return index * MAX_FROXEL_LIGHT_COUNT;
}
uint slice = getDepthSlice();
uint offset = getFroxelOffset(slice);
// Compute lighting...
Several uniforms must be precomputed for perform the index evaluation efficiently. The code used to precompute these uniforms can be found in listing ?.
froxels[0] = TILE_RESOLUTION_IN_PX;
froxels[1] = TILE_RESOLUTION_IN_PX;
froxels[2] = numberOfTilesInX;
froxels[3] = numberOfTilesInY;
zParams[0] = 1.0f  Z_FAR / Z_NEAR;
zParams[1] = Z_FAR / Z_NEAR;
zParams[2] = (MAX_DEPTH_SLICES  1) / log2(Z_SPECIAL_NEAR / Z_FAR);
zParams[3] = MAX_DEPTH_SLICES;
Given a froxel index \(i\), a special near plane \(sn\), a far plane \(f\) and a maximum number of depth slices \(m\), equation \(\ref{clusterToZ}\) computes the minimum depth of a given froxel.
$$\begin{equation}\label{clusterToZ} clusterToZ(i \ge 1,sn,f,m)=2^{(im) \frac{log2(\frac{sn}{f})}{m1}} \end{equation}$$
For \(i=0\), the z value is 0. The result of this equation is in the [0..1] range and should be multiplied by \(f\) to get a distance in world units.
The compute shader implementation should use exp2
instead of a pow
. The division can be precomputed and passed as a uniform.
Given the complexity of our lighting system, it is important to validate our implementation. We will do so in several ways: using reference renderings, light measurements and data visualization.
[TODO] Explain light measurement validation (reading EV from the render target and comparing against values measure with light meters/cameras, etc.)
A quick and easy way to validate a scene's lighting is to modify the shader to output colors that provide an intuitive mapping to relevant data. This can easily be done by using a custom debug tonemapping operator that outputs fake colors.
With emissive materials and IBLs, it is fairly easy to obtain a scene in which specular highlights are brighter than their apparent caster. This type of issue can be difficult to observe after tonemapping and quantization but is fairly obvious in the scenereferred space. Figure 84 shows how the custom operator described in listing 45 is used to show the exposed luminance of a scene.
vec3 Tonemap_DisplayRange(const vec3 x) {
// The 5th color in the array (cyan) represents middle gray (18%)
// Every stop above or below middle gray causes a color shift
float v = log2(luminance(x) / 0.18);
v = clamp(v + 5.0, 0.0, 15.0);
int index = int(floor(v));
return mix(debugColors[index], debugColors[min(15, index + 1)], fract(v));
}
const vec3 debugColors[16] = vec3[](
vec3(0.0, 0.0, 0.0), // black
vec3(0.0, 0.0, 0.1647), // darkest blue
vec3(0.0, 0.0, 0.3647), // darker blue
vec3(0.0, 0.0, 0.6647), // dark blue
vec3(0.0, 0.0, 0.9647), // blue
vec3(0.0, 0.9255, 0.9255), // cyan
vec3(0.0, 0.5647, 0.0), // dark green
vec3(0.0, 0.7843, 0.0), // green
vec3(1.0, 1.0, 0.0), // yellow
vec3(0.90588, 0.75294, 0.0), // yelloworange
vec3(1.0, 0.5647, 0.0), // orange
vec3(1.0, 0.0, 0.0), // bright red
vec3(0.8392, 0.0, 0.0), // red
vec3(1.0, 0.0, 1.0), // magenta
vec3(0.6, 0.3333, 0.7882), // purple
vec3(1.0, 1.0, 1.0) // white
);
To validate our implementation against reference renderings, we will use a commercialgrade Open Source physically based offline path tracer called Mitsuba. Mitsuba offers many different integrators, samplers and material models, which should allow us to provide fair comparisons with our realtime renderer. This path tracer also relies on a simple XML scene description format that should be easy to automatically generate from our own scene descriptions.
Figure 85 and figure 86 show a simple scene, a perfectly smooth dielectric sphere, rendered respectively with Mitsuba and Filament.
The parameters used to render both scenes are the following:
Filament
Mitsuba
The full Mitsuba scene can be found as an annex. Both scenes were rendered at the same resolution (2048×1440).
The slight differences between the two renderings come from the various approximations used by Filament: RGBM 256×256 reflection probe, RGBM 1024×1024 background map, Lambert diffuse, splitsum approximation, analytical approximation of the DFG term, etc.
Figure 87 shows the luminance gradient of the images produced by both engines. The comparison was performed on LDR images.
The biggest difference is visible at grazing angles, which is most likely explained by Filament's use of a Lambertian diffuse term. The Disney diffuse term and its grazing retroreflections would move Filament closer to Mitsuba.
Filament uses a Yup, righthanded coordinate system.
Filament's Camera looks towards its local Z axis. That is, when placing a camera in the world without any transform applied to it, the camera looks down the world's Z axis.
All cubemaps used in Filament follow the OpenGL convention for face alignment shown in figure 89.
Note that environment background and reflection probes are mirrored (see section 8.6.3.1).
To simplify the rendering of reflections, IBL cubemaps are stored mirrored on the X axis. This is
the default behaviour of the cmgen
tool. This means that an IBL cubemap used as environment
background needs to be mirrored again at runtime.
An easy way to achieve this for skyboxes is to use textured back faces. Filament does
this by default.
To convert equirectangular environment maps to horizontal/vertical cross cubemaps we position the +Z face in the center of the source rectilinear environment map.
When specifying a skybox or an IBL in Filament, the specified cubemap is oriented such that its Z face points towards the +Z axis of the world (this is because filament assumes mirrored cubemaps, see section 8.6.3.1). However, because environments and skyboxes are expected to be premirrored, their Z (back) face points towards the world's Z axis as expected (and the camera looks toward that direction by default, see section 8.6.2).
The specular color of a metallic surface, or \(\fNormal\), can be computed directly from measured spectral data. Online databases such as Refractive Index provide tables of complex IOR measured at different wavelengths for various materials.
Earlier in this document, we presented equation \(\ref{fresnelEquation}\) to compute the Fresnel reflectance at normal incidence for a dielectric surface given its IOR. The same equation can be rewritten for conductors by using complex numbers to represent the surface's IOR:
$$\begin{equation} c_{ior} = n_{ior} + ik \end{equation}$$
Equation \(\ref{fresnelComplexIOR}\) presents the resulting Fresnel formula, where \(c^*\) is the conjugate of the complex number \(c\):
$$\begin{equation}\label{fresnelComplexIOR} \fNormal(c_{ior}) = \frac{(c_{ior}  1)(c_{ior}^*  1)}{(c_{ior} + 1)(c_{ior}^* + 1)} \end{equation}$$
To compute the specular color of a material we need to evaluate the complex Fresnel equation at each spectral sample of complex IOR over the visible spectrum. For each spectral sample, we obtain a spectral reflectance sample. To find the RGB color at normal incidence, we must multiply each sample by the CIE XYZ CMFs (color matching functions) and the spectral power distribution of the desired illuminant. We choose the standard illuminant D65 because we want to compute a color in the sRGB color space.
We then sum (integrate) and normalize all the samples to obtain \(\fNormal\) in the XYZ color space. From there, a simple color space conversion yields a linear sRGB color or a nonlinear sRGB color after applying the optoelectronic transfer function (OETF, commonly known as “gamma” curve). Note that for some materials such as gold the final sRGB color might fall out of gamut. We use a simple normalization step as a cheap form of gamut remapping but it would be interesting to consider computing values in a color space with a wider gamut (for instance BT.2020).
To achieve the desired result we used the ICE 1931 2° CMFs, from 360nm to 830nm at 1nm intervals (source), and the CIE Standard Illuminant D65 relative spectral power distribution, from 300nm to 830nm, at 5nm intervals (source).
Our implementation is presented in listing 46, with the actual data omitted for brevity.
// CIE 1931 2deg color matching functions (CMFs), from 360nm to 830nm,
// at 1nm intervals
//
// Data source:
// http://cvrl.ioo.ucl.ac.uk/cmfs.htm
// http://cvrl.ioo.ucl.ac.uk/database/text/cmfs/ciexyz31.htm
const size_t CIE_XYZ_START = 360;
const size_t CIE_XYZ_COUNT = 471;
const float3 CIE_XYZ[CIE_XYZ_COUNT] = { ... };
// CIE Standard Illuminant D65 relative spectral power distribution,
// from 300nm to 830, at 5nm intervals
//
// Data source:
// https://en.wikipedia.org/wiki/Illuminant_D65
// https://cielab.xyz/pdf/CIE_sel_colorimetric_tables.xls
const size_t CIE_D65_INTERVAL = 5;
const size_t CIE_D65_START = 300;
const size_t CIE_D65_END = 830;
const size_t CIE_D65_COUNT = 107;
const float CIE_D65[CIE_D65_COUNT] = { ... };
struct Sample {
float w = 0.0f; // wavelength
std::complex<float> ior; // complex IOR, n + ik
};
static float illuminantD65(float w) {
auto i0 = size_t((w  CIE_D65_START) / CIE_D65_INTERVAL);
uint2 indexBounds{i0, std::min(i0 + 1, CIE_D65_END)};
float2 wavelengthBounds = CIE_D65_START + float2{indexBounds} * CIE_D65_INTERVAL;
float t = (w  wavelengthBounds.x) / (wavelengthBounds.y  wavelengthBounds.x);
return lerp(CIE_D65[indexBounds.x], CIE_D65[indexBounds.y], t);
}
// For std::lower_bound
bool operator<(const Sample& lhs, const Sample& rhs) {
return lhs.w < rhs.w;
}
// The wavelength w must be between 360nm and 830nm
static std::complex<float> findSample(const std::vector<sample>& samples, float w) {
auto i1 = std::lower_bound(
samples.begin(), samples.end(), Sample{w, 0.0f + 0.0if});
auto i0 = i1  1;
// Interpolate the complex IORs
float t = (w  i0>w) / (i1>w  i0>w);
float n = lerp(i0>ior.real(), i1>ior.real(), t);
float k = lerp(i0>ior.imag(), i1>ior.imag(), t);
return { n, k };
}
static float fresnel(const std::complex<float>& sample) {
return (((sample  (1.0f + 0if)) * (std::conj(sample)  (1.0f + 0if))) /
((sample + (1.0f + 0if)) * (std::conj(sample) + (1.0f + 0if)))).real();
}
static float3 XYZ_to_sRGB(const float3& v) {
const mat3f XYZ_sRGB{
3.2404542f, 0.9692660f, 0.0556434f,
1.5371385f, 1.8760108f, 0.2040259f,
0.4985314f, 0.0415560f, 1.0572252f
};
return XYZ_sRGB * v;
}
// Outputs a linear sRGB color
static float3 computeColor(const std::vector<sample>& samples) {
float3 xyz{0.0f};
float y = 0.0f;
for (size_t i = 0; i < CIE_XYZ_COUNT; i++) {
// Current wavelength
float w = CIE_XYZ_START + i;
// Find most appropriate CIE XYZ sample for the wavelength
auto sample = findSample(samples, w);
// Compute Fresnel reflectance at normal incidence
float f0 = fresnel(sample);
// We need to multiply by the spectral power distribution of the illuminant
float d65 = illuminantD65(w);
xyz += f0 * CIE_XYZ[i] * d65;
y += CIE_XYZ[i].y * d65;
}
// Normalize so that 100% reflectance at every wavelength yields Y=1
xyz /= y;
float3 linear = XYZ_to_sRGB(xyz);
// Normalize outofgamut values
if (any(greaterThan(linear, float3{1.0f}))) linear *= 1.0f / max(linear);
return linear;
}
Special thanks to Naty Hoffman for his valuable help on this topic.
In the discrete domain, the integral can be approximated with sampling as defined in equation \(\ref{iblSampling}\).
$$\begin{equation}\label{iblSampling} \Lout(n,v,\Theta) \equiv \frac{1}{N} \sum_{i}^{N} f(l_{i}^{uniform},v,\Theta) L_{\perp}(l_i) \left< n \cdot l_i^{uniform} \right> \end{equation}$$
Unfortunately, we would need too many samples to evaluate this integral. A technique commonly used is to choose samples that are more “important” more often, this is called importance sampling. In our case we'll use the distribution of microfacets normals, \(D_{ggx}\), as the distribution of important samples.
The evaluation of \( \Lout(n,v,\Theta) \) with importance sampling is presented in equation \(\ref{annexIblImportanceSampling}\).
$$\begin{equation}\label{annexIblImportanceSampling} \Lout(n,v,\Theta) \equiv \frac{1}{N} \sum_{i}^{N} \frac{f(l_{i},v,\Theta)}{p(l_i,v,\Theta)} L_{\perp}(l_i) \left< n \cdot l_i \right> \end{equation}$$
In equation \(\ref{annexIblImportanceSampling}\), \(p\) is the probability density function (PDF) of the distribution of important direction samples \(l_i\). These samples depend on \(h_i\), \(v\) and \(\alpha\). The definition of the PDF is shown in equation \(\ref{iblPDF}\).
\(h_i\) is given by the distribution we chose, see section 9.2.1 for more details.
The important direction samples \(l_i\) are calculated as the reflection of \(v\) around \(h_i\), and therefore do not have the same PDF as \(h_i\). The PDF of a transformed distribution is given by:
$$\begin{equation} p(T_r(x)) = p(x) J(T_r) \end{equation}$$
Where \(J(T_r)\) is the determinant of the Jacobian of the transform. In our case we're considering the transform from \(h_i\) to \(l_i\) and the determinant of its Jacobian is given in \ref{iblPDF}.
$$\begin{equation}\label{iblPDF} p(l,v,\Theta) = D(h,\alpha) \left< \NoH \right> J_{h \rightarrow l} \\ J_{h \rightarrow l} = \frac{1}{4 \left< \VoH \right>} \end{equation}$$
Refer to section 9.3 for more details. Given a uniform distribution \((\zeta_{\phi},\zeta_{\theta})\) the important direction \(l\) is defined by equation \(\ref{importantDirection}\).
$$\begin{equation}\label{importantDirection} \phi = 2 \pi \zeta_{\phi} \\ \theta = cos^{1} \sqrt{\frac{1  \zeta_{\theta}}{(\alpha^2  1)\zeta_{\theta}+1}} \\ l = \{ cos \phi sin \theta, sin \phi sin \theta, cos \theta \} \end{equation}$$
Typically, \( (\zeta_{\phi},\zeta_{\theta}) \) are chosen using the Hammersley uniform distribution algorithm described in section 9.4.
Importance sampling considers only the PDF to generate important directions; in particular, it is oblivious to the actual content of the IBL. If the latter contains high frequencies in areas without a lot of samples, the integration won’t be accurate. This can be somewhat mitigated by using a technique called prefiltered importance sampling, in addition this allows the integral to converge with many fewer samples.
Prefiltered importance sampling uses several images of the environment increasingly lowpass filtered. This is typically implemented very efficiently with mipmaps and a box filter. The LOD is selected based on the sample importance, that is, low probability samples use a higher LOD index (more filtered).
This technique is described in details in [Krivanek08].
The cubemap LOD is determined in the following way:
$$\begin{align*} lod &= log_4 \left( K\frac{\Omega_s}{\Omega_p} \right) \\ K &= 4.0 \\ \Omega_s &= \frac{1}{N \cdot p(l_i)} \\ \Omega_p &\approx \frac{4\pi}{6 \cdot width \cdot height} \end{align*}$$
Where \(K\) is a constant determined empirically, \(p\) the PDF of the BRDF, \( \Omega_{s} \) the solid angle associated to the sample and \(\Omega_p\) the solid angle associated with the texel in the cubemap.
Cubemap sampling is done using seamless trilinear filtering. It is extremely important to sample the cubemap correctly across faces using OpenGL's seamless sampling feature or any other technique that avoids/reduces seams.
Table 17 shows a comparison between importance sampling and prefiltered importance sampling when applied to figure 90.
The reference renderer used in the comparison below performs no approximation. In particular, it does not assume \(v = n\) and does not perform the split sum approximation. The prefiltered renderer uses all the techniques discussed in this section: prefiltered cubemaps, the analytic formulation of the DFG term, and of course the split sum approximation.
Left: reference renderer, right: prefiltered importance sampling.








For simplicity we use the \( D \) term of the BRDF as the PDF, however the PDF must be normalized such that the integral over the hemisphere is 1:
$$\begin{equation} \int_{\Omega}p(m)dm = 1 \\ \int_{\Omega}D(m)(n \cdot m)dm = 1 \\ \int_{\phi=0}^{2\pi}\int_{\theta=0}^{\frac{\pi}{2}}D(\theta,\phi) cos \theta sin \theta d\theta d\phi = 1 \\ \end{equation}$$
The PDF of the BRDF can therefore be expressed as in equation \(\ref{importantPDF}\):
$$\begin{equation}\label{importantPDF} p(\theta,\phi) = \frac{\alpha^2}{\pi(cos^2\theta (\alpha^21) + 1)^2} cos\theta sin\theta \end{equation}$$
The term \(sin\theta\) comes from the differential solid angle \(sin\theta d\phi d\theta\) since we integrate over a sphere. We sample \(\theta\) and \(\phi\) independently:
$$\begin{align*} p(\theta) &= \int_0^{2\pi} p(\theta,\phi) d\phi = \frac{2\alpha^2}{(cos^2\theta (\alpha^21) + 1)^2} cos\theta sin\theta \\ p(\phi) &= \frac{p(\theta,\phi)}{p(\phi)} = \frac{1}{2\pi} \end{align*}$$
The expression of \( p(\phi) \) is true for an isotropic distribution of normals.
We then calculate the cumulative distribution function (CDF) for each variable:
$$\begin{align*} P(s_{\phi}) &= \int_{0}^{s_{\phi}} p(\phi) d\phi = \frac{s_{\phi}}{2\pi} \\ P(s_{\theta}) &= \int_{0}^{s_{\theta}} p(\theta) d\theta = 2 \alpha^2 \left( \frac{1}{(2\alpha^44\alpha^2+2) cos(s_{\theta})^2 + 2\alpha^2  2}  \frac{1}{2\alpha^42\alpha^2} \right) \end{align*}$$
We set \( P(s_{\phi}) \) and \( P(s_{\theta}) \) to random variables \( \zeta_{\phi} \) and \( \zeta_{\theta} \) and solve for \( s_{\phi} \) and \( s_{\theta} \) respectively:
$$\begin{align*} P(s_{\phi}) &= \zeta_{\phi} \rightarrow s_{\phi} = 2\pi\zeta_{\phi} \\ P(s_{\theta}) &= \zeta_{\theta} \rightarrow s_{\theta} = cos^{1} \sqrt{\frac{1\zeta_{\theta}}{(\alpha^21)\zeta_{\theta}+1}} \end{align*}$$
So given a uniform distribution \( (\zeta_{\phi},\zeta_{\theta}) \), our important direction \(l\) is defined as:
$$\begin{align*} \phi &= 2\pi\zeta_{\phi} \\ \theta &= cos^{1} \sqrt{\frac{1\zeta_{\theta}}{(\alpha^21)\zeta_{\theta}+1}} \\ l &= \{ cos\phi sin\theta,sin\phi sin\theta,cos\theta \} \end{align*}$$
vec2f hammersley(uint i, float numSamples) {
uint bits = i;
bits = (bits << 16)  (bits >> 16);
bits = ((bits & 0x55555555) << 1)  ((bits & 0xAAAAAAAA) >> 1);
bits = ((bits & 0x33333333) << 2)  ((bits & 0xCCCCCCCC) >> 2);
bits = ((bits & 0x0F0F0F0F) << 4)  ((bits & 0xF0F0F0F0) >> 4);
bits = ((bits & 0x00FF00FF) << 8)  ((bits & 0xFF00FF00) >> 8);
return vec2f(i / numSamples, bits / exp2(32));
}
The term \( L_{DFG} \) is only dependent on \( \NoV \). Below, the normal is arbitrarily set to \( n=\left[0, 0, 1\right] \) and \(v\) is chosen to satisfy \( \NoV \). The vector \( h_i \) is the \( D_{GGX}(\alpha) \) important direction sample \(i\).
float GDFG(float NoV, float NoL, float a) {
float a2 = a * a;
float GGXL = NoV * sqrt((NoL * a2 + NoL) * NoL + a2);
float GGXV = NoL * sqrt((NoV * a2 + NoV) * NoV + a2);
return (2 * NoL) / (GGXV + GGXL);
}
float2 DFG(float NoV, float a) {
float3 V;
V.x = sqrt(1.0f  NoV*NoV);
V.y = 0.0f;
V.z = NoV;
float2 r = 0.0f;
for (uint i = 0; i < sampleCount; i++) {
float2 Xi = hammersley(i, sampleCount);
float3 H = importanceSampleGGX(Xi, a, N);
float3 L = 2.0f * dot(V, H) * H  V;
float VoH = saturate(dot(V, H));
float NoL = saturate(L.z);
float NoH = saturate(H.z);
if (NoL > 0.0f) {
float G = GDFG(NoV, NoL, a);
float Gv = G * VoH / NoH;
float Fc = pow(1  VoH, 5.0f);
r.x += Gv * (1  Fc);
r.y += Gv * Fc;
}
}
return r * (1.0f / sampleCount);
}
Symbol  Definition 

\(K^m_l\)  Normalization factors 
\(P^m_l(x)\)  Associated Legendre polynomials 
\(y^m_l\)  Spherical harmonics bases, or SH bases 
\(L^m_l\)  SH coefficients of the \(L(s)\) function defined on the unit sphere 
Spherical parameterization of points on the surface of the unit sphere:
$$\begin{equation} \{ x, y, z \} = \{ cos \phi sin \theta, sin \phi sin \theta, cos \theta \} \end{equation}$$
The complex spherical harmonics bases are given by:
$$\begin{equation} Y^m_l(\theta, \phi) = K^m_l e^{im\theta} P^{m}_l(cos \theta), l \in N, l <= m <= l \end{equation}$$
However we only need the real bases:
$$\begin{align*} y^{m > 0}_l &= \sqrt{2} K^m_l cos(m \phi) P^m_l(cos \theta) \\ y^{m < 0}_l &= \sqrt{2} K^m_l sin(m \phi) P^{m}_l(cos \theta) \\ y^0_l &= K^0_l P^0_l(cos \theta) \end{align*}$$
The normalization factors are given by:
$$\begin{equation} K^m_l = \sqrt{\frac{(2l + 1)(l  m)!}{4 \pi (l + m)!}} \end{equation}$$
The associated Legendre polynomials \(P^{m}_l\) can be calculated from the following recursions:
$$\begin{equation}\label{shRecursions} P^0_0(x) = 1 \\ P^0_1(x) = x \\ P^l_l(x) = (1)^l (2l  1)!! (1  x^2)^{\frac{l}{2}} \\ P^m_l(x) = \frac{((2l  1) x P^m_{l  1}  (l + m  1) P^m_{l  2})}{l  m} \\ \end{equation}$$
Computing \(y^{m}_l\) requires to compute \(P^{m}_l(z)\) first. This can be accomplished fairly easily using the recursions in equation \(\ref{shRecursions}\). The third recursion can be used to “move diagonally” in table 20, i.e. calculating \(y^0_0\), \(y^1_1\), \(y^2_2\) etc. Then, the fourth recursion can be used to move vertically.
Band index  Basis functions \(l <= m <= l\) 

\(l = 0\)  \(y^0_0\) 
\(l = 1\)  \(y^{1}_1\) \(y^0_1\) \(y^1_1\) 
\(l = 2\)  \(y^{2}_2\) \(y^{1}_2\) \(y^0_2\) \(y^1_2\) \(y^2_2\) 
It’s also fairly easy to compute the trigonometric terms recursively:
$$\begin{align*} C_m &\equiv cos(m \phi) \\ S_m &\equiv sin(m \phi) \\ \{ x, y, z \} &= \{ cos \phi sin \theta, sin \phi sin \theta, cos \theta \} \end{align*}$$
Using the angle sum trigonometric identities:
$$\begin{align*} cos(m \phi + \phi) &= cos(m \phi) cos(\phi)  sin(m \phi) sin(\phi) \Leftrightarrow C_{m + 1} = \frac{(x C_m  y S_m)}{sin(\theta)^{m + 1}} \\ sin(m \phi + \phi) &= sin(m \phi) sin(\phi) + cos(m \phi) sin(\phi) \Leftrightarrow S_{m + 1} = \frac{(x S_m  y C_m)}{sin(\theta)^{m + 1}} \end{align*}$$
The equations above have an extra term \(sin(\theta)^{m + 1}\) but we can compensate for that in the \(P^{m}_l(z)\) recursion by multiplying \(P^l_l(z)\) by \(sin(\theta)^{m + 1}\) which greatly simplifies the third equation in \(\ref{shRecursions}\) because \(P^l_l(cos \theta) sin(\theta)^{l} = (1)^l(2l  1)!!\).
Listing 47 shows the C++ code to compute the nonnormalized SH basis \(\frac{y^m_l(s)}{\sqrt{2} K^m_l}\):
static inline size_t SHindex(ssize_t m, size_t l) {
return l * (l + 1) + m;
}
void computeShBasis(
double* const SHb,
size_t numBands,
const vec3& s)
{
// handle m=0 separately, since it produces only one coefficient
double Pml_2 = 0;
double Pml_1 = 1;
SHb[0] = Pml_1;
for (ssize_t l = 1; l < numBands; l++) {
double Pml = ((2 * l  1) * Pml_1 * s.z  (l  1) * Pml_2) / l;
Pml_2 = Pml_1;
Pml_1 = Pml;
SHb[SHindex(0, l)] = Pml;
}
double Pmm = 1;
for (ssize_t m = 1; m < numBands ; m++) {
Pmm = (1  2 * m) * Pmm;
double Pml_2 = Pmm;
double Pml_1 = (2 * m + 1)*Pmm*s.z;
// l == m
SHb[SHindex(m, m)] = Pml_2;
SHb[SHindex( m, m)] = Pml_2;
if (m + 1 < numBands) {
// l == m+1
SHb[SHindex(m, m + 1)] = Pml_1;
SHb[SHindex( m, m + 1)] = Pml_1;
for (ssize_t l = m + 2; l < numBands; l++) {
double Pml = ((2 * l  1) * Pml_1 * s.z  (l + m  1) * Pml_2)
/ (l  m);
Pml_2 = Pml_1;
Pml_1 = Pml;
SHb[SHindex(m, l)] = Pml;
SHb[SHindex( m, l)] = Pml;
}
}
}
double Cm = s.x;
double Sm = s.y;
for (ssize_t m = 1; m <= numBands ; m++) {
for (ssize_t l = m; l < numBands ; l++) {
SHb[SHindex(m, l)] *= Sm;
SHb[SHindex( m, l)] *= Cm;
}
double Cm1 = Cm * s.x  Sm * s.y;
double Sm1 = Sm * s.x + Cm * s.y;
Cm = Cm1;
Sm = Sm1;
}
}
Normalized SH basis functions \(y^m_l(s)\) for the first 3 bands:
Band  \(m = 2\)  \(m = 1\)  \(m = 0\)  \(m = 1\)  \(m = 2\) 

\(l = 0\)  \(\frac{1}{2}\sqrt{\frac{1}{\pi}}\)  
\(l = 1\)  \(\frac{1}{2}\sqrt{\frac{3}{\pi}}y\)  \(\frac{1}{2}\sqrt{\frac{3}{\pi}}z\)  \(\frac{1}{2}\sqrt{\frac{3}{\pi}}x\)  
\(l = 2\)  \(\frac{1}{2}\sqrt{\frac{15}{\pi}}xy\)  \(\frac{1}{2}\sqrt{\frac{15}{\pi}}yz\)  \(\frac{1}{4}\sqrt{\frac{5}{\pi}}(2z^2  x^2  y^2)\)  \(\frac{1}{2}\sqrt{\frac{15}{\pi}}xz\)  \(\frac{1}{4}\sqrt{\frac{15}{\pi}}(x^2  y^2)\) 
A function \(L(s)\) defined on a sphere is projected to the SH basis as follows:
$$\begin{equation} L^m_l = \int_\Omega L(s) y^m_l(s) ds \\ L^m_l = \int_{\theta = 0}^{\pi} \int_{\phi = 0}^{2\pi} L(\theta, \phi) y^m_l(\theta, \phi) sin \theta d\theta d\phi \end{equation}$$
Note that each \(L^m_l\) is a vector of 3 values, one for each RGB color channel.
The inverse transformation, or reconstruction, or rendering, from the SH coefficients is given by:
$$\begin{equation} \hat{L}(s) = \sum_l \sum_{m = l}^l L^m_l y^m_l(s) \end{equation}$$
Since \(\left< cos \theta \right>\) does not depend on \(\phi\) (azimuthal independence), the integral simplifies to:
$$\begin{align*} C^0_l &= 2\pi \int_0^{\pi} \left< cos \theta \right> y^0_l(\theta) sin \theta d\theta \\ C^0_l &= 2\pi K^)_l \int_0^{\frac{\pi}{2}} P^0_l(cos \theta) cos \theta sin \theta d\theta \\ C^m_l &= 0, m != 0 \end{align*}$$
In [Ramamoorthi01] an analytical solution to the integral is described:
$$\begin{align*} C_1 &= \sqrt{\frac{\pi}{3}} \\ C_{odd} &= 0 \\ C_{l, even} &= 2\pi \sqrt{\frac{2l + 1}{4\pi}} \frac{(1)^{\frac{l}{2}  1}}{(l + 2)(l  1)} \frac{l!}{2^l (\frac{l!}{2})^2} \end{align*}$$
The first few coefficients are:
$$\begin{align*} C_0 &= +0.88623 \\ C_1 &= +1.02333 \\ C_2 &= +0.49542 \\ C_3 &= +0.00000 \\ C_4 &= 0.11078 \end{align*}$$
Very few coefficients are needed to reasonably approximate \(\left< cos \theta \right>\), as shown in figure 91.
Convolutions by a kernel \(h\) that has a circular symmetry can be applied directly and easily in SH space:
$$\begin{equation} (h * f)^m_l = \sqrt{\frac{4\pi}{2l + 1}} h^0_l(s) f^m_l(s) \end{equation}$$
Conveniently, \(\sqrt{\frac{4\pi}{2l + 1}} = \frac{1}{K^0_l}\), so in practice we premultiply \(C_l\) by \(\frac{1}{K^0_l}\) and we get a simpler expression:
$$\begin{equation} \hat{C}_{l, even} = 2\pi \frac{(1)^{\frac{l}{2}  1}}{(l + 2)(l  1)} \frac{l!}{2^l (\frac{l!}{2})^2} \\ \hat{C}_1 = \frac{2\pi}{3} \end{equation}$$
Here is the C++ code to compute \(\hat{C}_l\):
static double factorial(size_t n, size_t d = 1);
// < cos(theta) > SH coefficients premultiplied by 1 / K(0,l)
double computeTruncatedCosSh(size_t l) {
if (l == 0) {
return M_PI;
} else if (l == 1) {
return 2 * M_PI / 3;
} else if (l & 1) {
return 0;
}
const size_t l_2 = l / 2;
double A0 = ((l_2 & 1) ? 1.0 : 1.0) / ((l + 2) * (l  1));
double A1 = factorial(l, l_2) / (factorial(l_2) * (1 << l));
return 2 * M_PI * A0 * A1;
}
// returns n! / d!
double factorial(size_t n, size_t d ) {
d = std::max(size_t(1), d);
n = std::max(size_t(1), n);
double r = 1.0;
if (n == d) {
// intentionally left blank
} else if (n > d) {
for ( ; n>d ; n) {
r *= n;
}
} else {
for ( ; d>n ; d) {
r *= d;
}
r = 1.0 / r;
}
return r;
}
<scene version="0.5.0">
<integrator type="path"/>
<shape type="serialized" id="sphere_mesh">
<string name="filename" value="plastic_sphere.serialized"/>
<integer name="shapeIndex" value="0"/>
<bsdf type="roughplastic">
<string name="distribution" value="ggx"/>
<float name="alpha" value="0.0"/>
<srgb name="diffuseReflectance" value="0.81, 0.0, 0.0"/>
</bsdf>
</shape>
<emitter type="envmap">
<string name="filename" value="../../environments/office/office.exr"/>
<float name="scale" value="35000.0" />
<boolean name="cache" value="false" />
</emitter>
<emitter type="directional">
<vector name="direction" x="1" y="1" z="1" />
<rgb name="irradiance" value="120000.0, 115200.0, 114000.0" />
</emitter>
<sensor type="perspective">
<float name="farClip" value="12.0"/>
<float name="focusDistance" value="4.1"/>
<float name="fov" value="45"/>
<string name="fovAxis" value="y"/>
<float name="nearClip" value="0.01"/>
<transform name="toWorld">
<lookat target="0, 0, 0" origin="0, 0, 3.1" up="0, 1, 0"/>
</transform>
<sampler type="ldsampler">
<integer name="sampleCount" value="256"/>
</sampler>
<film type="ldrfilm">
<integer name="height" value="1440"/>
<integer name="width" value="2048"/>
<float name="exposure" value="15.23" />
<rfilter type="gaussian"/>
</film>
</sensor>
</scene>
Assigning lights to froxels can be implemented on the GPU using two compute shaders. The first one, shown in listing 48, creates the froxels data (4 planes + a min Z and max Z per froxel) in an SSBO and needs to be run only once. The shader requires the following uniforms:
The projection matrix used to render the scene (view space to clip space transformation).  
The inverse of the projection matrix used to render the scene (clip space to view space transformation).  
\(log2(\frac{z_{lighnear}}{z_{far}}) \frac{1}{maxSlices1}\), maximum number of depth slices, Z near and Z far.  
\(\frac{F_x \times F_r}{w} \times 2\), with \(F_x\) the number of tiles on the X axis, \(F_r\) the resolution in pixels of a tile and w the width in pixels of the render target. 
#version 310 es
precision highp float;
precision highp int;
#define FROXEL_RESOLUTION 80u
layout(local_size_x = 1, local_size_y = 1, local_size_z = 1) in;
layout(location = 0) uniform mat4 projectionMatrix;
layout(location = 1) uniform mat4 projectionInverseMatrix;
layout(location = 2) uniform vec4 depthParams; // index scale, index bias, near, far
layout(location = 3) uniform float clipSpaceSize;
struct Froxel {
// NOTE: the planes should be stored in vec4[4] but the
// Adreno shader compiler has a bug that causes the data
// to not be read properly inside the loop
vec4 plane0;
vec4 plane1;
vec4 plane2;
vec4 plane3;
vec2 minMaxZ;
};
layout(binding = 0, std140) writeonly restrict buffer FroxelBuffer {
Froxel data[];
} froxels;
shared vec4 corners[4];
shared vec2 minMaxZ;
vec4 projectionToView(vec4 p) {
p = projectionInverseMatrix * p;
return p / p.w;
}
vec4 createPlane(vec4 b, vec4 c) {
// standard plane equation, with a at (0, 0, 0)
return vec4(normalize(cross(c.xyz, b.xyz)), 1.0);
}
void main() {
uint index = gl_WorkGroupID.x + gl_WorkGroupID.y * gl_NumWorkGroups.x +
gl_WorkGroupID.z * gl_NumWorkGroups.x * gl_NumWorkGroups.y;
if (gl_LocalInvocationIndex == 0u) {
// first tile the screen and build the frustum for the current tile
vec2 renderTargetSize = vec2(FROXEL_RESOLUTION * gl_NumWorkGroups.xy);
vec2 frustumMin = vec2(FROXEL_RESOLUTION * gl_WorkGroupID.xy);
vec2 frustumMax = vec2(FROXEL_RESOLUTION * (gl_WorkGroupID.xy + 1u));
corners[0] = vec4(
frustumMin.x / renderTargetSize.x * clipSpaceSize  1.0,
(renderTargetSize.y  frustumMin.y) / renderTargetSize.y
* clipSpaceSize  1.0,
1.0,
1.0
);
corners[1] = vec4(
frustumMax.x / renderTargetSize.x * clipSpaceSize  1.0,
(renderTargetSize.y  frustumMin.y) / renderTargetSize.y
* clipSpaceSize  1.0,
1.0,
1.0
);
corners[2] = vec4(
frustumMax.x / renderTargetSize.x * clipSpaceSize  1.0,
(renderTargetSize.y  frustumMax.y) / renderTargetSize.y
* clipSpaceSize  1.0,
1.0,
1.0
);
corners[3] = vec4(
frustumMin.x / renderTargetSize.x * clipSpaceSize  1.0,
(renderTargetSize.y  frustumMax.y) / renderTargetSize.y
* clipSpaceSize  1.0,
1.0,
1.0
);
uint froxelSlice = gl_WorkGroupID.z;
minMaxZ = vec2(0.0, 0.0);
if (froxelSlice > 0u) {
minMaxZ.x = exp2((float(froxelSlice)  depthParams.y) * depthParams.x)
* depthParams.w;
}
minMaxZ.y = exp2((float(froxelSlice + 1u)  depthParams.y) * depthParams.x)
* depthParams.w;
}
if (gl_LocalInvocationIndex == 0u) {
vec4 frustum[4];
frustum[0] = projectionToView(corners[0]);
frustum[1] = projectionToView(corners[1]);
frustum[2] = projectionToView(corners[2]);
frustum[3] = projectionToView(corners[3]);
froxels.data[index].plane0 = createPlane(frustum[0], frustum[1]);
froxels.data[index].plane1 = createPlane(frustum[1], frustum[2]);
froxels.data[index].plane2 = createPlane(frustum[2], frustum[3]);
froxels.data[index].plane3 = createPlane(frustum[3], frustum[0]);
froxels.data[index].minMaxZ = minMaxZ;
}
}
The second compute shader, shown in listing 49, runs every frame (if the camera and/or lights have changed) and assigns all the lights to their respective froxels. This shader relies only on a couple of uniforms (the number of point/spot lights and the view matrix) and four SSBOs:
For each froxel, the index of each light that affects said froxel. The indices for point lights are written first and if there is enough space left, the indices for spot lights are written as well. A sentinel of value 0×7fffffffu separates point and spot lights and/or marks the end of the froxel's list of lights. Each froxel has a maximum number of lights (point + spot).
Array of structures describing the scene's point lights.
Array of structures describing the scene's spot lights.
The list of froxels represented by planes, created by the previous compute shader.
#version 310 es
precision highp float;
precision highp int;
#define LIGHT_BUFFER_SENTINEL 0x7fffffffu
#define MAX_FROXEL_LIGHT_COUNT 32u
#define THREADS_PER_FROXEL_X 8u
#define THREADS_PER_FROXEL_Y 8u
#define THREADS_PER_FROXEL_Z 1u
#define THREADS_PER_FROXEL (THREADS_PER_FROXEL_X * \
THREADS_PER_FROXEL_Y * THREADS_PER_FROXEL_Z)
layout(local_size_x = THREADS_PER_FROXEL_X,
local_size_y = THREADS_PER_FROXEL_Y,
local_size_z = THREADS_PER_FROXEL_Z) in;
// x = point lights, y = spot lights
layout(location = 0) uniform uvec2 totalLightCount;
layout(location = 1) uniform mat4 viewMatrix;
layout(binding = 0, packed) writeonly restrict buffer LightIndexBuffer {
uint index[];
} lightIndexBuffer;
struct PointLight {
vec4 positionFalloff; // x, y, z, falloff
vec4 colorIntensity; // r, g, b, intensity
vec4 directionIES; // dir x, dir y, dir z, IES profile index
};
layout(binding = 1, std140) readonly restrict buffer PointLightBuffer {
PointLight lights[];
} pointLights;
struct SpotLight {
vec4 positionFalloff; // x, y, z, falloff
vec4 colorIntensity; // r, g, b, intensity
vec4 directionIES; // dir x, dir y, dir z, IES profile index
vec4 angle; // angle scale, angle offset, unused, unused
};
layout(binding = 2, std140) readonly restrict buffer SpotLightBuffer {
SpotLight lights[];
} spotLights;
struct Froxel {
// NOTE: the planes should be stored in vec4[4] but the
// Adreno shader compiler has a bug that causes the data
// to not be read properly inside the loop
vec4 plane0;
vec4 plane1;
vec4 plane2;
vec4 plane3;
vec2 minMaxZ;
};
layout(binding = 3, std140) readonly restrict buffer FroxelBuffer {
Froxel data[];
} froxels;
shared uint groupLightCounter;
shared uint groupLightIndexBuffer[MAX_FROXEL_LIGHT_COUNT];
float signedDistanceFromPlane(vec4 p, vec4 plane) {
// plane.w == 0.0, simplify computation
return dot(plane.xyz, p.xyz);
}
void synchronize() {
memoryBarrierShared();
barrier();
}
void main() {
if (gl_LocalInvocationIndex == 0u) {
groupLightCounter = 0u;
}
memoryBarrierShared();
uint froxelIndex = gl_WorkGroupID.x + gl_WorkGroupID.y * gl_NumWorkGroups.x +
gl_WorkGroupID.z * gl_NumWorkGroups.x * gl_NumWorkGroups.y;
Froxel current = froxels.data[froxelIndex];
uint offset = gl_LocalInvocationID.x +
gl_LocalInvocationID.y * THREADS_PER_FROXEL_X;
for (uint i = 0u; i < totalLightCount.x &&
groupLightCounter < MAX_FROXEL_LIGHT_COUNT &&
offset + i < totalLightCount.x; i += THREADS_PER_FROXEL) {
uint currentLight = offset + i;
vec4 center = pointLights.lights[currentLight].positionFalloff;
center.xyz = (viewMatrix * vec4(center.xyz, 1.0)).xyz;
float r = inversesqrt(center.w);
if (center.z + r > current.minMaxZ.x &&
center.z  r <= current.minMaxZ.y) {
if (signedDistanceFromPlane(center, current.plane0) < r &&
signedDistanceFromPlane(center, current.plane1) < r &&
signedDistanceFromPlane(center, current.plane2) < r &&
signedDistanceFromPlane(center, current.plane3) < r) {
uint index = atomicAdd(groupLightCounter, 1u);
groupLightIndexBuffer[index] = currentLight;
}
}
}
synchronize();
uint pointLightCount = groupLightCounter;
offset = froxelIndex * MAX_FROXEL_LIGHT_COUNT;
for (uint i = gl_LocalInvocationIndex; i < pointLightCount;
i += THREADS_PER_FROXEL) {
lightIndexBuffer.index[offset + i] = groupLightIndexBuffer[i];
}
if (gl_LocalInvocationIndex == 0u) {
if (pointLightCount < MAX_FROXEL_LIGHT_COUNT) {
lightIndexBuffer.index[offset + pointLightCount] = LIGHT_BUFFER_SENTINEL;
}
}
}
Wednesday 20 Feb 2019  Cloth shading 
 
Tuesday 21 Aug 2018  Multiscattering 
 
Friday 17 Aug 2018  Specular color 
 
Wednesday 15 Aug 2018  Fresnel 
 
Thursday 9 Aug 2018  Lighting 
 
Tuesday 7 Aug 2018  Cloth model 
 
Friday 3 Aug 2018  First public version 
