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Lib PBR Aniso - Shader API | Substance 3D Painter

Lib PBR Aniso - Shader API

lib-pbr-aniso.glsl

Public Functions: normal_distrib G1 visibility cook_torrance_contrib importanceSampleGGX probabilityGGX pbrComputeSpecularAnisotropic

Import from library

import lib-pbr.glsl
import lib-pbr.glsl
import lib-pbr.glsl

BRDF related functions

float normal_distrib(
vec3 localH,
vec2 alpha)
{
localH.xy /= alpha;
float tmp = dot(localH, localH);
return 1.0 / (M_PI * alpha.x * alpha.y * tmp * tmp);
}
float G1(
vec3 localW,
vec2 alpha)
{
// One generic factor of the geometry function divided by ndw
localW.xy *= alpha;
return 2.0 / (localW.z + length(localW));
}
float visibility(
vec3 localL,
vec3 localV,
vec2 alpha)
{
// visibility is a Cook-Torrance geometry function divided by (n.l)*(n.v)
return G1(localL, alpha) * G1(localV, alpha);
}
vec3 cook_torrance_contrib(
float vdh,
float ndh,
vec3 localL,
vec3 localE,
vec3 Ks,
vec2 alpha)
{
// This is the contribution when using importance sampling with the GGX based
// sample distribution. This means ct_contrib = ct_brdf / ggx_probability
return fresnel(vdh, Ks) * (visibility(localL, localE, alpha) * vdh * localL.z / ndh);
}
vec3 importanceSampleGGX(vec2 Xi, vec2 alpha)
{
float phi = 2.0 * M_PI * Xi.x;
vec2 slope = sqrt(Xi.y / (1.0 - Xi.y)) * alpha * vec2(cos(phi), sin(phi));
return normalize(vec3(slope, 1.0));
}
float probabilityGGX(vec3 localH, float vdh, vec2 alpha)
{
return normal_distrib(localH, alpha) * localH.z / (4.0 * vdh);
}
vec3 pbrComputeSpecularAnisotropic(LocalVectors vectors, vec3 specColor, vec2 roughness)
{
vec3 radiance = vec3(0.0);
vec2 alpha = roughness * roughness;
mat3 TBN = mat3(vectors.tangent, vectors.bitangent, vectors.normal);
vec3 localE = vectors.eye * TBN;
for(int i=0; i<nbSamples; ++i)
{
vec2 Xi = fibonacci2DDitheredTemporal(i, nbSamples);
vec3 localH = importanceSampleGGX(Xi, alpha);
vec3 localL = reflect(-localE, localH);
if (localL.z > 0.0)
{
vec3 Ln = TBN * localL;
float vdh = max(1e-8, dot(localE, localH));
float fade = horizonFading(dot(vectors.vertexNormal, Ln), horizonFade);
float pdf = probabilityGGX(localH, vdh, alpha);
float lodS = max(roughness.x, roughness.y) < 0.01 ? 0.0 : computeLOD(Ln, pdf);
// Offset lodS to trade bias for more noise
lodS -= 1.0;
vec3 preconvolvedSample = envSampleLOD(Ln, lodS);
radiance +=
fade * cook_torrance_contrib(vdh, localH.z, localL, localE, specColor, alpha) *
preconvolvedSample;
}
}
return radiance / float(nbSamples);
}
float normal_distrib( vec3 localH, vec2 alpha) { localH.xy /= alpha; float tmp = dot(localH, localH); return 1.0 / (M_PI * alpha.x * alpha.y * tmp * tmp); } float G1( vec3 localW, vec2 alpha) { // One generic factor of the geometry function divided by ndw localW.xy *= alpha; return 2.0 / (localW.z + length(localW)); } float visibility( vec3 localL, vec3 localV, vec2 alpha) { // visibility is a Cook-Torrance geometry function divided by (n.l)*(n.v) return G1(localL, alpha) * G1(localV, alpha); } vec3 cook_torrance_contrib( float vdh, float ndh, vec3 localL, vec3 localE, vec3 Ks, vec2 alpha) { // This is the contribution when using importance sampling with the GGX based // sample distribution. This means ct_contrib = ct_brdf / ggx_probability return fresnel(vdh, Ks) * (visibility(localL, localE, alpha) * vdh * localL.z / ndh); } vec3 importanceSampleGGX(vec2 Xi, vec2 alpha) { float phi = 2.0 * M_PI * Xi.x; vec2 slope = sqrt(Xi.y / (1.0 - Xi.y)) * alpha * vec2(cos(phi), sin(phi)); return normalize(vec3(slope, 1.0)); } float probabilityGGX(vec3 localH, float vdh, vec2 alpha) { return normal_distrib(localH, alpha) * localH.z / (4.0 * vdh); } vec3 pbrComputeSpecularAnisotropic(LocalVectors vectors, vec3 specColor, vec2 roughness) { vec3 radiance = vec3(0.0); vec2 alpha = roughness * roughness; mat3 TBN = mat3(vectors.tangent, vectors.bitangent, vectors.normal); vec3 localE = vectors.eye * TBN; for(int i=0; i<nbSamples; ++i) { vec2 Xi = fibonacci2DDitheredTemporal(i, nbSamples); vec3 localH = importanceSampleGGX(Xi, alpha); vec3 localL = reflect(-localE, localH); if (localL.z > 0.0) { vec3 Ln = TBN * localL; float vdh = max(1e-8, dot(localE, localH)); float fade = horizonFading(dot(vectors.vertexNormal, Ln), horizonFade); float pdf = probabilityGGX(localH, vdh, alpha); float lodS = max(roughness.x, roughness.y) < 0.01 ? 0.0 : computeLOD(Ln, pdf); // Offset lodS to trade bias for more noise lodS -= 1.0; vec3 preconvolvedSample = envSampleLOD(Ln, lodS); radiance += fade * cook_torrance_contrib(vdh, localH.z, localL, localE, specColor, alpha) * preconvolvedSample; } } return radiance / float(nbSamples); }
float normal_distrib( 
  vec3 localH, 
  vec2 alpha) 
{ 
  localH.xy /= alpha; 
  float tmp = dot(localH, localH); 
  return 1.0 / (M_PI * alpha.x * alpha.y * tmp * tmp); 
} 
 
float G1( 
  vec3 localW, 
  vec2 alpha) 
{ 
  // One generic factor of the geometry function divided by ndw 
  localW.xy *= alpha; 
  return 2.0 / (localW.z + length(localW)); 
} 
 
float visibility( 
  vec3 localL, 
  vec3 localV, 
  vec2 alpha) 
{ 
  // visibility is a Cook-Torrance geometry function divided by (n.l)*(n.v) 
  return G1(localL, alpha) * G1(localV, alpha); 
} 
 
vec3 cook_torrance_contrib( 
  float vdh, 
  float ndh, 
  vec3 localL, 
  vec3 localE, 
  vec3 Ks, 
  vec2 alpha) 
{ 
  // This is the contribution when using importance sampling with the GGX based 
  // sample distribution. This means ct_contrib = ct_brdf / ggx_probability 
  return fresnel(vdh, Ks) * (visibility(localL, localE, alpha) * vdh * localL.z / ndh); 
} 
 
vec3 importanceSampleGGX(vec2 Xi, vec2 alpha) 
{ 
  float phi = 2.0 * M_PI * Xi.x; 
  vec2 slope = sqrt(Xi.y / (1.0 - Xi.y)) * alpha * vec2(cos(phi), sin(phi)); 
  return normalize(vec3(slope, 1.0)); 
} 
 
float probabilityGGX(vec3 localH, float vdh, vec2 alpha) 
{ 
  return normal_distrib(localH, alpha) * localH.z / (4.0 * vdh); 
} 
 
vec3 pbrComputeSpecularAnisotropic(LocalVectors vectors, vec3 specColor, vec2 roughness) 
{ 
  vec3 radiance = vec3(0.0); 
  vec2 alpha = roughness * roughness; 
  mat3 TBN = mat3(vectors.tangent, vectors.bitangent, vectors.normal); 
  vec3 localE = vectors.eye * TBN; 
 
  for(int i=0; i<nbSamples; ++i) 
  { 
    vec2 Xi = fibonacci2DDitheredTemporal(i, nbSamples); 
    vec3 localH = importanceSampleGGX(Xi, alpha); 
    vec3 localL = reflect(-localE, localH); 
 
    if (localL.z > 0.0) 
    { 
      vec3 Ln = TBN * localL; 
      float vdh = max(1e-8, dot(localE, localH)); 
 
      float fade = horizonFading(dot(vectors.vertexNormal, Ln), horizonFade); 
      float pdf = probabilityGGX(localH, vdh, alpha); 
      float lodS = max(roughness.x, roughness.y) < 0.01 ? 0.0 : computeLOD(Ln, pdf); 
      // Offset lodS to trade bias for more noise 
      lodS -= 1.0; 
      vec3 preconvolvedSample = envSampleLOD(Ln, lodS); 
 
      radiance += 
        fade * cook_torrance_contrib(vdh, localH.z, localL, localE, specColor, alpha) * 
        preconvolvedSample; 
    } 
  } 
 
  return radiance / float(nbSamples); 
} 
 

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