BansheeEngine/Data/Raw/Engine/Includes/ImportanceSampling.bslinc

mixin ImportanceSampling
{
	code
	{
		#define PI 3.1415926
	
		float radicalInverse(uint bits)  
		{
			// Reverse bits. Algorithm from Hacker's Delight.
			bits = (bits << 16u) | (bits >> 16u);
			bits = ((bits & 0x55555555u) << 1u) | ((bits & 0xAAAAAAAAu) >> 1u);
			bits = ((bits & 0x33333333u) << 2u) | ((bits & 0xCCCCCCCCu) >> 2u);
			bits = ((bits & 0x0F0F0F0Fu) << 4u) | ((bits & 0xF0F0F0F0u) >> 4u);
			bits = ((bits & 0x00FF00FFu) << 8u) | ((bits & 0xFF00FF00u) >> 8u);
			
			// Normalizes unsigned int in range [0, 4294967295] to [0, 1]
			return float(bits) * 2.3283064365386963e-10;
		}
		
		float2 hammersleySequence(uint i, uint count)
		{
			float2 output;
			output.x = i / (float)count;
			output.y = radicalInverse(i);
			
			return output;
		}
		
		// Returns cos(theta) in x and phi in y
		float2 importanceSampleGGX(float2 e, float roughness4)
		{
			// See GGXImportanceSample.nb for derivation (essentially, take base GGX, normalize it,
			// generate PDF, split PDF into marginal probability for theta and conditional probability
			// for phi. Plug those into the CDF, invert it.)				
			float cosTheta = sqrt((1.0f - e.x) / (1.0f + (roughness4 - 1.0f) * e.x));
			float phi = 2.0f * PI * e.y;
			
			return float2(cosTheta, phi);
		}
		
		float3 sphericalToCartesian(float cosTheta, float sinTheta, float phi)
		{
			float3 output;
			output.x = sinTheta * cos(phi);
			output.y = sinTheta * sin(phi);
			output.z = cosTheta;
			
			return output;
		}
		
		float pdfGGX(float cosTheta, float sinTheta, float roughness4)
		{
			float d = (cosTheta*roughness4 - cosTheta) * cosTheta + 1;
			return roughness4 * cosTheta * sinTheta / (d*d*PI);
		}
	};
};