mult_axis_MIS.cpp

float BSSRDF::MISWeight(const Fragment& fo, const Fragment& fi,
    BSSRDFSampleAxis mainAxis, float pdf, float sigmaTr,
    float Rmax) const {
    float weight = 0.0f;
    // The U, V, N ratio is 1 : 1 : 2, and we do MIS with
    // power heuristic, so 1: 1: 4 
    const Vector3& pwo = fo.getPosition();
    const Vector3& pwi = fi.getPosition();
    const Vector3& ni = fi.getNormal();
    if(mainAxis == NAxis) {
        const Vector3& u = normalize(fo.getDPDU());
        const Vector3& v = normalize(fo.getDPDV());
        float UPdf = 0.25f * 
            gaussianSample2DPdf(pwo, pwi, u, sigmaTr, Rmax) * 
            absdot(u, ni);
        float VPdf = 0.25f * 
            gaussianSample2DPdf(pwo, pwi, v, sigmaTr, Rmax) *
            absdot(v, ni);
        float numerator = 4 *  pdf * pdf;
        weight = numerator / (numerator + UPdf* UPdf + VPdf * VPdf);
    } else if(mainAxis == UAxis) {
        const Vector3& n = fo.getNormal();
        const Vector3& v = normalize(fo.getDPDV());
        float NPdf = 0.5f * 
            gaussianSample2DPdf(pwo, pwi, n, sigmaTr, Rmax) * 
            absdot(n, ni);
        float VPdf = 0.25f * 
            gaussianSample2DPdf(pwo, pwi, v, sigmaTr, Rmax) *
            absdot(v, ni);
        float numerator = pdf * pdf;
        weight = numerator / (4 * NPdf * NPdf + numerator + VPdf * VPdf);
    } else if(mainAxis == VAxis) {
        const Vector3& n = fo.getNormal();
        const Vector3& u = normalize(fo.getDPDU());
        float NPdf = 0.5f * 
            gaussianSample2DPdf(pwo, pwi, n, sigmaTr, Rmax) * 
            absdot(n, ni);
        float UPdf = 0.25f * 
            gaussianSample2DPdf(pwo, pwi, u, sigmaTr, Rmax) *
            absdot(u, ni);
        float numerator = pdf * pdf;
        weight = numerator / (4 * NPdf * NPdf + UPdf * UPdf + numerator);
    }
    return weight;
}