diff options
author | Peter Kim <pk15950@gmail.com> | 2022-09-08 07:00:12 +0300 |
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committer | Peter Kim <pk15950@gmail.com> | 2022-09-08 07:00:12 +0300 |
commit | 00dcfdf916c69672210b006e62d966f1bc2fbeb7 (patch) | |
tree | 0cbb1b91fe26c750197126085b74224a795a103c /intern/cycles/kernel/sample/jitter.h | |
parent | a39532670f6b668da7be5810fb1f844b82feeba3 (diff) | |
parent | d5934974219135102f364f57c45a8b1465e2b8d9 (diff) |
Merge branch 'master' into xr-devxr-dev
Diffstat (limited to 'intern/cycles/kernel/sample/jitter.h')
-rw-r--r-- | intern/cycles/kernel/sample/jitter.h | 231 |
1 files changed, 73 insertions, 158 deletions
diff --git a/intern/cycles/kernel/sample/jitter.h b/intern/cycles/kernel/sample/jitter.h index b8da94248a4..e748f95fc7d 100644 --- a/intern/cycles/kernel/sample/jitter.h +++ b/intern/cycles/kernel/sample/jitter.h @@ -1,182 +1,97 @@ /* SPDX-License-Identifier: Apache-2.0 * Copyright 2011-2022 Blender Foundation */ +#include "kernel/sample/util.h" +#include "util/hash.h" + #pragma once CCL_NAMESPACE_BEGIN -ccl_device_inline uint32_t laine_karras_permutation(uint32_t x, uint32_t seed) +ccl_device float pmj_sample_1D(KernelGlobals kg, + uint sample, + const uint rng_hash, + const uint dimension) { - x += seed; - x ^= (x * 0x6c50b47cu); - x ^= x * 0xb82f1e52u; - x ^= x * 0xc7afe638u; - x ^= x * 0x8d22f6e6u; + uint seed = rng_hash; - return x; -} + /* Use the same sample sequence seed for all pixels when using + * scrambling distance. */ + if (kernel_data.integrator.scrambling_distance < 1.0f) { + seed = kernel_data.integrator.seed; + } -ccl_device_inline uint32_t nested_uniform_scramble(uint32_t x, uint32_t seed) -{ - x = reverse_integer_bits(x); - x = laine_karras_permutation(x, seed); - x = reverse_integer_bits(x); + /* Shuffle the pattern order and sample index to better decorrelate + * dimensions and make the most of the finite patterns we have. + * The funky sample mask stuff is to ensure that we only shuffle + * *within* the current sample pattern, which is necessary to avoid + * early repeat pattern use. */ + const uint pattern_i = hash_shuffle_uint(dimension, NUM_PMJ_PATTERNS, seed); + /* NUM_PMJ_SAMPLES should be a power of two, so this results in a mask. */ + const uint sample_mask = NUM_PMJ_SAMPLES - 1; + const uint sample_shuffled = nested_uniform_scramble(sample, + hash_wang_seeded_uint(dimension, seed)); + sample = (sample & ~sample_mask) | (sample_shuffled & sample_mask); + + /* Fetch the sample. */ + const uint index = ((pattern_i * NUM_PMJ_SAMPLES) + sample) % + (NUM_PMJ_SAMPLES * NUM_PMJ_PATTERNS); + float x = kernel_data_fetch(sample_pattern_lut, index * 2); + + /* Do limited Cranley-Patterson rotation when using scrambling distance. */ + if (kernel_data.integrator.scrambling_distance < 1.0f) { + const float jitter_x = hash_wang_seeded_float(dimension, rng_hash) * + kernel_data.integrator.scrambling_distance; + x += jitter_x; + x -= floorf(x); + } return x; } -ccl_device_inline uint cmj_hash(uint i, uint p) +ccl_device float2 pmj_sample_2D(KernelGlobals kg, + uint sample, + const uint rng_hash, + const uint dimension) { - i ^= p; - i ^= i >> 17; - i ^= i >> 10; - i *= 0xb36534e5; - i ^= i >> 12; - i ^= i >> 21; - i *= 0x93fc4795; - i ^= 0xdf6e307f; - i ^= i >> 17; - i *= 1 | p >> 18; - - return i; -} - -ccl_device_inline uint cmj_hash_simple(uint i, uint p) -{ - i = (i ^ 61) ^ p; - i += i << 3; - i ^= i >> 4; - i *= 0x27d4eb2d; - return i; -} - -ccl_device_inline float cmj_randfloat(uint i, uint p) -{ - return cmj_hash(i, p) * (1.0f / 4294967808.0f); -} - -ccl_device_inline float cmj_randfloat_simple(uint i, uint p) -{ - return cmj_hash_simple(i, p) * (1.0f / (float)0xFFFFFFFF); -} + uint seed = rng_hash; -ccl_device_inline float cmj_randfloat_simple_dist(uint i, uint p, float d) -{ - return cmj_hash_simple(i, p) * (d / (float)0xFFFFFFFF); -} - -ccl_device float pmj_sample_1D(KernelGlobals kg, uint sample, uint rng_hash, uint dimension) -{ - uint hash = rng_hash; - float jitter_x = 0.0f; + /* Use the same sample sequence seed for all pixels when using + * scrambling distance. */ if (kernel_data.integrator.scrambling_distance < 1.0f) { - hash = kernel_data.integrator.seed; - - jitter_x = cmj_randfloat_simple_dist( - dimension, rng_hash, kernel_data.integrator.scrambling_distance); + seed = kernel_data.integrator.seed; } - /* Perform Owen shuffle of the sample number to reorder the samples. */ -#ifdef _SIMPLE_HASH_ - const uint rv = cmj_hash_simple(dimension, hash); -#else /* Use a _REGULAR_HASH_. */ - const uint rv = cmj_hash(dimension, hash); -#endif -#ifdef _XOR_SHUFFLE_ -# warning "Using XOR shuffle." - const uint s = sample ^ rv; -#else /* Use _OWEN_SHUFFLE_ for reordering. */ - const uint s = nested_uniform_scramble(sample, rv); -#endif - - /* Based on the sample number a sample pattern is selected and offset by the dimension. */ - const uint sample_set = s / NUM_PMJ_SAMPLES; - const uint d = (dimension + sample_set); - const uint dim = d % NUM_PMJ_PATTERNS; - - /* The PMJ sample sets contain a sample with (x,y) with NUM_PMJ_SAMPLES so for 1D - * the x part is used for even dims and the y for odd. */ - int index = 2 * ((dim >> 1) * NUM_PMJ_SAMPLES + (s % NUM_PMJ_SAMPLES)) + (dim & 1); - - float fx = kernel_tex_fetch(__sample_pattern_lut, index); - -#ifndef _NO_CRANLEY_PATTERSON_ROTATION_ - /* Use Cranley-Patterson rotation to displace the sample pattern. */ -# ifdef _SIMPLE_HASH_ - float dx = cmj_randfloat_simple(d, hash); -# else - float dx = cmj_randfloat(d, hash); -# endif - /* Jitter sample locations and map back into [0 1]. */ - fx = fx + dx + jitter_x; - fx = fx - floorf(fx); -#else -# warning "Not using Cranley-Patterson Rotation." -#endif - - return fx; -} - -ccl_device void pmj_sample_2D(KernelGlobals kg, - uint sample, - uint rng_hash, - uint dimension, - ccl_private float *x, - ccl_private float *y) -{ - uint hash = rng_hash; - float jitter_x = 0.0f; - float jitter_y = 0.0f; + /* Shuffle the pattern order and sample index to better decorrelate + * dimensions and make the most of the finite patterns we have. + * The funky sample mask stuff is to ensure that we only shuffle + * *within* the current sample pattern, which is necessary to avoid + * early repeat pattern use. */ + const uint pattern_i = hash_shuffle_uint(dimension, NUM_PMJ_PATTERNS, seed); + /* NUM_PMJ_SAMPLES should be a power of two, so this results in a mask. */ + const uint sample_mask = NUM_PMJ_SAMPLES - 1; + const uint sample_shuffled = nested_uniform_scramble(sample, + hash_wang_seeded_uint(dimension, seed)); + sample = (sample & ~sample_mask) | (sample_shuffled & sample_mask); + + /* Fetch the sample. */ + const uint index = ((pattern_i * NUM_PMJ_SAMPLES) + sample) % + (NUM_PMJ_SAMPLES * NUM_PMJ_PATTERNS); + float x = kernel_data_fetch(sample_pattern_lut, index * 2); + float y = kernel_data_fetch(sample_pattern_lut, index * 2 + 1); + + /* Do limited Cranley-Patterson rotation when using scrambling distance. */ if (kernel_data.integrator.scrambling_distance < 1.0f) { - hash = kernel_data.integrator.seed; - - jitter_x = cmj_randfloat_simple_dist( - dimension, rng_hash, kernel_data.integrator.scrambling_distance); - jitter_y = cmj_randfloat_simple_dist( - dimension + 1, rng_hash, kernel_data.integrator.scrambling_distance); + const float jitter_x = hash_wang_seeded_float(dimension, rng_hash) * + kernel_data.integrator.scrambling_distance; + const float jitter_y = hash_wang_seeded_float(dimension, rng_hash ^ 0xca0e1151) * + kernel_data.integrator.scrambling_distance; + x += jitter_x; + y += jitter_y; + x -= floorf(x); + y -= floorf(y); } - /* Perform a shuffle on the sample number to reorder the samples. */ -#ifdef _SIMPLE_HASH_ - const uint rv = cmj_hash_simple(dimension, hash); -#else /* Use a _REGULAR_HASH_. */ - const uint rv = cmj_hash(dimension, hash); -#endif -#ifdef _XOR_SHUFFLE_ -# warning "Using XOR shuffle." - const uint s = sample ^ rv; -#else /* Use _OWEN_SHUFFLE_ for reordering. */ - const uint s = nested_uniform_scramble(sample, rv); -#endif - - /* Based on the sample number a sample pattern is selected and offset by the dimension. */ - const uint sample_set = s / NUM_PMJ_SAMPLES; - const uint d = dimension + sample_set; - uint dim = d % NUM_PMJ_PATTERNS; - int index = 2 * (dim * NUM_PMJ_SAMPLES + (s % NUM_PMJ_SAMPLES)); - - float fx = kernel_tex_fetch(__sample_pattern_lut, index); - float fy = kernel_tex_fetch(__sample_pattern_lut, index + 1); - -#ifndef _NO_CRANLEY_PATTERSON_ROTATION_ - /* Use Cranley-Patterson rotation to displace the sample pattern. */ -# ifdef _SIMPLE_HASH_ - float dx = cmj_randfloat_simple(d, hash); - float dy = cmj_randfloat_simple(d + 1, hash); -# else - float dx = cmj_randfloat(d, hash); - float dy = cmj_randfloat(d + 1, hash); -# endif - /* Jitter sample locations and map back to the unit square [0 1]x[0 1]. */ - float sx = fx + dx + jitter_x; - float sy = fy + dy + jitter_y; - sx = sx - floorf(sx); - sy = sy - floorf(sy); -#else -# warning "Not using Cranley Patterson Rotation." -#endif - - (*x) = sx; - (*y) = sy; + return make_float2(x, y); } CCL_NAMESPACE_END |