static real ChiSquare(creal x, cint df)
{
real y;
if( df <= 0 ) return -999;
if( x <= 0 ) return 0;
if( x > 1000*df ) return 1;
if( df > 1000 ) {
if( x < 2 ) return 0;
y = 2./(9*df);
y = (powx(x/df, 1/3.) - (1 - y))/sqrtx(y);
if( y > 5 ) return 1;
if( y < -18.8055 ) return 0;
return Normal(y);
}
y = .5*x;
if( df & 1 ) {
creal sqrty = sqrtx(y);
real h = Erf(sqrty);
count i;
if( df == 1 ) return h;
y = sqrty*expx(-y)/.8862269254527579825931;
for( i = 3; i < df; i += 2 ) {
h -= y;
y *= x/i;
}
y = h - y;
}
else {
real term = expx(-y), sum = term;
count i;
for( i = 1; i < df/2; ++i )
sum += term *= y/i;
y = 1 - sum;
}
return Max(0., y);
}
inline hvl_float CalcB1pB2(const HVL_Context *ctx, const hvl_float &a3, const hvl_float &sqrtz)
{
const hvl_float b1 = ctx->HVL_ONE / (Exp(a3) - ctx->HVL_ONE);
const hvl_float b2 = ctx->HVL_HALF * (ctx->HVL_ONE + Erf(sqrtz));
return b1 + b2;
}
inline double HVL_da3_internal(const HVL_Context *ctx,
const hvl_float &a0, const hvl_float &a3,
const hvl_float &gauss_base, const hvl_float &b1pb2, const hvl_float &reca3, const hvl_float &sqrtz)
{
if (a3 == ctx->HVL_ZERO)
return ToDouble(-ctx->HVL_HALF * a0 * Erf(sqrtz) * gauss_base);
const hvl_float a3e = Exp(a3);
const hvl_float nmr = gauss_base * a0 * (-reca3 + (a3e / (Pow(a3e - ctx->HVL_ONE, 2) * b1pb2)));
return ToDouble(reca3 * nmr / b1pb2);
}
// Microfacet Utility Functions
static void BeckmannSample11(Float cosThetaI, Float U1, Float U2,
Float *slope_x, Float *slope_y) {
/* Special case (normal incidence) */
if (cosThetaI > .9999) {
Float r = std::sqrt(-std::log(1.0f - U1));
Float sinPhi = std::sin(2 * Pi * U2);
Float cosPhi = std::cos(2 * Pi * U2);
*slope_x = r * cosPhi;
*slope_y = r * sinPhi;
}
/* The original inversion routine from the paper contained
discontinuities, which causes issues for QMC integration
and techniques like Kelemen-style MLT. The following code
performs a numerical inversion with better behavior */
Float sinThetaI =
std::sqrt(std::max((Float)0, (Float)1 - cosThetaI * cosThetaI));
Float tanThetaI = sinThetaI / cosThetaI;
Float cotThetaI = 1 / tanThetaI;
/* Search interval -- everything is parameterized
in the Erf() domain */
Float a = -1, c = Erf(cotThetaI);
Float sample_x = std::max(U1, (Float)1e-6f);
/* Start with a good initial guess */
// Float b = (1-sample_x) * a + sample_x * c;
/* We can do better (inverse of an approximation computed in
* Mathematica) */
Float thetaI = std::acos(cosThetaI);
Float fit = 1 + thetaI * (-0.876f + thetaI * (0.4265f - 0.0594f * thetaI));
Float b = c - (1 + c) * std::pow(1 - sample_x, fit);
/* Normalization factor for the CDF */
static const Float SQRT_PI_INV = 1.f / std::sqrt(Pi);
Float normalization =
1 /
(1 + c + SQRT_PI_INV * tanThetaI * std::exp(-cotThetaI * cotThetaI));
int it = 0;
while (++it < 10) {
/* Bisection criterion -- the oddly-looking
Boolean expression are intentional to check
for NaNs at little additional cost */
if (!(b >= a && b <= c)) b = 0.5f * (a + c);
/* Evaluate the CDF and its derivative
(i.e. the density function) */
Float invErf = ErfInv(b);
Float value =
normalization *
(1 + b + SQRT_PI_INV * tanThetaI * std::exp(-invErf * invErf)) -
sample_x;
Float derivative = normalization * (1 - invErf * tanThetaI);
if (std::abs(value) < 1e-5f) break;
/* Update bisection intervals */
if (value > 0)
c = b;
else
a = b;
b -= value / derivative;
}
/* Now convert back into a slope value */
*slope_x = ErfInv(b);
/* Simulate Y component */
*slope_y = ErfInv(2.0f * std::max(U2, (Float)1e-6f) - 1.0f);
Assert(!std::isinf(*slope_x));
Assert(!std::isnan(*slope_x));
Assert(!std::isinf(*slope_y));
Assert(!std::isnan(*slope_y));
}
static inline real Normal(creal x)
{
return .5*Erf(x/1.414213562373095048801689) + .5;
}
