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Diffstat (limited to 'intern/cycles/kernel/sample/sample_pattern.h')
| -rw-r--r-- | intern/cycles/kernel/sample/sample_pattern.h | 185 |
1 files changed, 185 insertions, 0 deletions
diff --git a/intern/cycles/kernel/sample/sample_pattern.h b/intern/cycles/kernel/sample/sample_pattern.h new file mode 100644 index 00000000000..95635c2c855 --- /dev/null +++ b/intern/cycles/kernel/sample/sample_pattern.h @@ -0,0 +1,185 @@ +/* + * Copyright 2011-2013 Blender Foundation + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include "kernel/sample/sample_jitter.h" +#include "util/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__ + +/* High Dimensional Sobol. + * + * Multidimensional sobol with generator matrices. Dimension 0 and 1 are equal + * to classic Van der Corput and Sobol sequences. */ + +#ifdef __SOBOL__ + +/* Skip initial numbers that for some dimensions have clear patterns that + * don't cover the entire sample space. Ideally we would have a better + * progressive pattern that doesn't suffer from this problem, because even + * with this offset some dimensions are quite poor. + */ +# define SOBOL_SKIP 64 + +ccl_device uint sobol_dimension(KernelGlobals kg, int index, int dimension) +{ + uint result = 0; + uint i = index + SOBOL_SKIP; + for (int j = 0, x; (x = find_first_set(i)); i >>= x) { + j += x; + result ^= __float_as_uint(kernel_tex_fetch(__sample_pattern_lut, 32 * dimension + j - 1)); + } + return result; +} + +#endif /* __SOBOL__ */ + +ccl_device_forceinline float path_rng_1D(KernelGlobals kg, + uint rng_hash, + int sample, + int dimension) +{ +#ifdef __DEBUG_CORRELATION__ + return (float)drand48(); +#endif + +#ifdef __SOBOL__ + if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_PMJ) +#endif + { + return pmj_sample_1D(kg, sample, rng_hash, dimension); + } + +#ifdef __SOBOL__ + /* Sobol sequence value using direction vectors. */ + uint result = sobol_dimension(kg, sample, dimension); + float r = (float)result * (1.0f / (float)0xFFFFFFFF); + + /* Cranly-Patterson rotation using rng seed */ + float shift; + + /* Hash rng with dimension to solve correlation issues. + * See T38710, T50116. + */ + uint tmp_rng = cmj_hash_simple(dimension, rng_hash); + shift = tmp_rng * (1.0f / (float)0xFFFFFFFF); + + return r + shift - floorf(r + shift); +#endif +} + +ccl_device_forceinline void path_rng_2D(KernelGlobals kg, + uint rng_hash, + int sample, + int dimension, + ccl_private float *fx, + ccl_private float *fy) +{ +#ifdef __DEBUG_CORRELATION__ + *fx = (float)drand48(); + *fy = (float)drand48(); + return; +#endif + +#ifdef __SOBOL__ + if (kernel_data.integrator.sampling_pattern == SAMPLING_PATTERN_PMJ) +#endif + { + pmj_sample_2D(kg, sample, rng_hash, dimension, fx, fy); + + return; + } + +#ifdef __SOBOL__ + /* Sobol. */ + *fx = path_rng_1D(kg, rng_hash, sample, dimension); + *fy = path_rng_1D(kg, rng_hash, sample, dimension + 1); +#endif +} + +/** + * 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; +} + +ccl_device_inline bool sample_is_even(int pattern, int sample) +{ + if (pattern == SAMPLING_PATTERN_PMJ) { + /* See Section 10.2.1, "Progressive Multi-Jittered Sample Sequences", Christensen et al. + * We can use this to get divide sample sequence into two classes for easier variance + * estimation. */ +#if defined(__GNUC__) && !defined(__KERNEL_GPU__) + return __builtin_popcount(sample & 0xaaaaaaaa) & 1; +#elif defined(__NVCC__) + return __popc(sample & 0xaaaaaaaa) & 1; +#else + /* TODO(Stefan): pop-count intrinsic for Windows with fallback for older CPUs. */ + int i = sample & 0xaaaaaaaa; + i = i - ((i >> 1) & 0x55555555); + i = (i & 0x33333333) + ((i >> 2) & 0x33333333); + i = (((i + (i >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24; + return i & 1; +#endif + } + else { + /* TODO(Stefan): Are there reliable ways of dividing CMJ and Sobol into two classes? */ + return sample & 0x1; + } +} + +CCL_NAMESPACE_END |