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/* SPDX-License-Identifier: Apache-2.0
* Copyright 2011-2022 Blender Foundation */
#pragma once
#include "kernel/sample/jitter.h"
#include "kernel/sample/sobol_burley.h"
#include "util/hash.h"
CCL_NAMESPACE_BEGIN
/* Pseudo random numbers, uncomment this for debugging correlations. Only run
* this single threaded on a CPU for repeatable results. */
//#define __DEBUG_CORRELATION__
ccl_device_forceinline float path_rng_1D(KernelGlobals kg,
uint rng_hash,
int sample,
int dimension)
{
#ifdef __DEBUG_CORRELATION__
return (float)drand48();
#endif
if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_SOBOL_BURLEY) {
return sobol_burley_sample_1D(sample, dimension, rng_hash);
}
else {
return pmj_sample_1D(kg, sample, rng_hash, dimension);
}
}
ccl_device_forceinline float2 path_rng_2D(KernelGlobals kg,
uint rng_hash,
int sample,
int dimension)
{
#ifdef __DEBUG_CORRELATION__
return make_float2((float)drand48(), (float)drand48());
#endif
if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_SOBOL_BURLEY) {
return sobol_burley_sample_2D(sample, dimension, rng_hash);
}
else {
return pmj_sample_2D(kg, sample, rng_hash, dimension);
}
}
/**
* 1D hash recommended from "Hash Functions for GPU Rendering" JCGT Vol. 9, No. 3, 2020
* See https://www.shadertoy.com/view/4tXyWN and https://www.shadertoy.com/view/XlGcRh
* http://www.jcgt.org/published/0009/03/02/paper.pdf
*/
ccl_device_inline uint hash_iqint1(uint n)
{
n = (n << 13U) ^ n;
n = n * (n * n * 15731U + 789221U) + 1376312589U;
return n;
}
/**
* 2D hash recommended from "Hash Functions for GPU Rendering" JCGT Vol. 9, No. 3, 2020
* See https://www.shadertoy.com/view/4tXyWN and https://www.shadertoy.com/view/XlGcRh
* http://www.jcgt.org/published/0009/03/02/paper.pdf
*/
ccl_device_inline uint hash_iqnt2d(const uint x, const uint y)
{
const uint qx = 1103515245U * ((x >> 1U) ^ (y));
const uint qy = 1103515245U * ((y >> 1U) ^ (x));
const uint n = 1103515245U * ((qx) ^ (qy >> 3U));
return n;
}
ccl_device_inline uint path_rng_hash_init(KernelGlobals kg,
const int sample,
const int x,
const int y)
{
const uint rng_hash = hash_iqnt2d(x, y) ^ kernel_data.integrator.seed;
#ifdef __DEBUG_CORRELATION__
srand48(rng_hash + sample);
#else
(void)sample;
#endif
return rng_hash;
}
/**
* Splits samples into two different classes, A and B, which can be
* compared for variance estimation.
*/
ccl_device_inline bool sample_is_class_A(int pattern, int sample)
{
#if 0
if (!(pattern == SAMPLING_PATTERN_PMJ || pattern == SAMPLING_PATTERN_SOBOL_BURLEY)) {
/* Fallback: assign samples randomly.
* This is guaranteed to work "okay" for any sampler, but isn't good.
* (NOTE: the seed constant is just a random number to guard against
* possible interactions with other uses of the hash. There's nothing
* special about it.)
*/
return hash_hp_seeded_uint(sample, 0xa771f873) & 1;
}
#else
(void)pattern;
#endif
/* This follows the approach from section 10.2.1 of "Progressive
* Multi-Jittered Sample Sequences" by Christensen et al., but
* implemented with efficient bit-fiddling.
*
* This approach also turns out to work equally well with Sobol-Burley
* (see https://developer.blender.org/D15746#429471).
*/
return popcount(uint(sample) & 0xaaaaaaaa) & 1;
}
CCL_NAMESPACE_END
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