03b5be4e3c
This resolves some issues with correlation artifacts at higher sample counts. Fix T101356, correlation issues in new PMJ pattern. Differential Revision: https://developer.blender.org/D16561
91 lines
3.3 KiB
C
91 lines
3.3 KiB
C
/* SPDX-License-Identifier: Apache-2.0
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* Copyright 2011-2022 Blender Foundation */
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#include "kernel/sample/util.h"
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#include "util/hash.h"
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#pragma once
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CCL_NAMESPACE_BEGIN
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ccl_device uint pmj_shuffled_sample_index(KernelGlobals kg, uint sample, uint dimension, uint seed)
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{
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const uint sample_count = kernel_data.integrator.pmj_sequence_size;
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/* Shuffle the pattern order and sample index to better decorrelate
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* dimensions and make the most of the finite patterns we have.
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* The funky sample mask stuff is to ensure that we only shuffle
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* *within* the current sample pattern, which is necessary to avoid
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* early repeat pattern use. */
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const uint pattern_i = hash_shuffle_uint(dimension, NUM_PMJ_PATTERNS, seed);
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/* sample_count should always be a power of two, so this results in a mask. */
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const uint sample_mask = sample_count - 1;
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const uint sample_shuffled = nested_uniform_scramble(sample,
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hash_wang_seeded_uint(dimension, seed));
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sample = (sample & ~sample_mask) | (sample_shuffled & sample_mask);
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return ((pattern_i * sample_count) + sample) % (sample_count * NUM_PMJ_PATTERNS);
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}
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ccl_device float pmj_sample_1D(KernelGlobals kg,
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uint sample,
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const uint rng_hash,
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const uint dimension)
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{
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uint seed = rng_hash;
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/* Use the same sample sequence seed for all pixels when using
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* scrambling distance. */
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if (kernel_data.integrator.scrambling_distance < 1.0f) {
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seed = kernel_data.integrator.seed;
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}
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/* Fetch the sample. */
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const uint index = pmj_shuffled_sample_index(kg, sample, dimension, seed);
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float x = kernel_data_fetch(sample_pattern_lut, index * NUM_PMJ_DIMENSIONS);
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/* Do limited Cranley-Patterson rotation when using scrambling distance. */
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if (kernel_data.integrator.scrambling_distance < 1.0f) {
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const float jitter_x = hash_wang_seeded_float(dimension, rng_hash) *
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kernel_data.integrator.scrambling_distance;
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x += jitter_x;
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x -= floorf(x);
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}
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return x;
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}
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ccl_device float2 pmj_sample_2D(KernelGlobals kg,
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uint sample,
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const uint rng_hash,
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const uint dimension)
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{
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uint seed = rng_hash;
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/* Use the same sample sequence seed for all pixels when using
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* scrambling distance. */
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if (kernel_data.integrator.scrambling_distance < 1.0f) {
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seed = kernel_data.integrator.seed;
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}
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/* Fetch the sample. */
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const uint index = pmj_shuffled_sample_index(kg, sample, dimension, seed);
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float x = kernel_data_fetch(sample_pattern_lut, index * NUM_PMJ_DIMENSIONS);
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float y = kernel_data_fetch(sample_pattern_lut, index * NUM_PMJ_DIMENSIONS + 1);
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/* Do limited Cranley-Patterson rotation when using scrambling distance. */
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if (kernel_data.integrator.scrambling_distance < 1.0f) {
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const float jitter_x = hash_wang_seeded_float(dimension, rng_hash) *
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kernel_data.integrator.scrambling_distance;
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const float jitter_y = hash_wang_seeded_float(dimension, rng_hash ^ 0xca0e1151) *
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kernel_data.integrator.scrambling_distance;
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x += jitter_x;
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y += jitter_y;
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x -= floorf(x);
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y -= floorf(y);
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}
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return make_float2(x, y);
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}
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CCL_NAMESPACE_END
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