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-rw-r--r--intern/cycles/kernel/sample/jitter.h231
1 files changed, 73 insertions, 158 deletions
diff --git a/intern/cycles/kernel/sample/jitter.h b/intern/cycles/kernel/sample/jitter.h
index b5cfa624406..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_data_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_data_fetch(sample_pattern_lut, index);
- float fy = kernel_data_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
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