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test2/intern/cycles/kernel/sample/util.h

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/* SPDX-FileCopyrightText: 2011-2022 Blender Foundation
*
* SPDX-License-Identifier: Apache-2.0 */
#pragma once
#include "util/math.h"
#include "util/types.h"
CCL_NAMESPACE_BEGIN
/*
Cycles: improve Progressive Multi-Jittered sampling Fix two issues in the previous implementation: * Only power-of-two prefixes were progressively stratified, not suffixes. This resulted in unnecessarily increased noise when using non-power-of-two sample counts. * In order to try to get away with just a single sample pattern, the code used a combination of sample index shuffling and Cranley-Patterson rotation. Index shuffling is normally fine, but due to the sample patterns themselves not being quite right (as described above) this actually resulted in additional increased noise. Cranley-Patterson, on the other hand, always increases noise with randomized (t,s) nets like PMJ02, and should be avoided with these kinds of sequences. Addressed with the following changes: * Replace the sample pattern generation code with a much simpler algorithm recently published in the paper "Stochastic Generation of (t, s) Sample Sequences". This new implementation is easier to verify, produces fully progressively stratified PMJ02, and is *far* faster than the previous code, being O(N) in the number of samples generated. * It keeps the sample index shuffling, which works correctly now due to the improved sample patterns. But it now uses a newer high-quality hash instead of the original Laine-Karras hash. * The scrambling distance feature cannot (to my knowledge) be implemented with any decorrelation strategy other than Cranley-Patterson, so Cranley-Patterson is still used when that feature is enabled. But it is now disabled otherwise, since it increases noise. * In place of Cranley-Patterson, multiple independent patterns are generated and randomly chosen for different pixels and dimensions as described in the original PMJ paper. In this patch, the pattern selection is done via hash-based shuffling to ensure there are no repeats within a single pixel until all patterns have been used. The combination of these fixes brings the quality of Cycles' PMJ sampler in line with the previously submitted Sobol-Burley sampler in D15679. They are essentially indistinguishable in terms of quality/noise, which is expected since they are both randomized (0,2) sequences. Differential Revision: https://developer.blender.org/D15746
2022-08-23 20:48:48 +02:00
* Performs base-2 Owen scrambling on a reversed-bit unsigned integer.
*
* This is equivalent to the Laine-Karras permutation, but much higher
* quality. See https://psychopath.io/post/2021_01_30_building_a_better_lk_hash
*/
ccl_device_inline uint reversed_bit_owen(uint n, const uint seed)
{
n ^= n * 0x3d20adea;
n += seed;
n *= (seed >> 16) | 1;
n ^= n * 0x05526c56;
n ^= n * 0x53a22864;
return n;
}
Cycles: Implement blue-noise dithered sampling This patch implements blue-noise dithered sampling as described by Nathan Vegdahl (https://psychopath.io/post/2022_07_24_owen_scrambling_based_dithered_blue_noise_sampling), which in turn is based on "Screen-Space Blue-Noise Diffusion of Monte Carlo Sampling Error via Hierarchical Ordering of Pixels"(https://repository.kaust.edu.sa/items/1269ae24-2596-400b-a839-e54486033a93). The basic idea is simple: Instead of generating independent sequences for each pixel by scrambling them, we use a single sequence for the entire image, with each pixel getting one chunk of the samples. The ordering across pixels is determined by hierarchical scrambling of the pixel's position along a space-filling curve, which ends up being pretty much the same operation as already used for the underlying sequence. This results in a more high-frequency noise distribution, which appears smoother despite not being less noisy overall. The main limitation at the moment is that the improvement is only clear if the full sample amount is used per pixel, so interactive preview rendering and adaptive sampling will not receive the benefit. One exception to this is that when using the new "Automatic" setting, the first sample in interactive rendering will also be blue-noise-distributed. The sampling mode option is now exposed in the UI, with the three options being Blue Noise (the new mode), Classic (the previous Tabulated Sobol method) and the new default, Automatic (blue noise, with the additional property of ensuring the first sample is also blue-noise-distributed in interactive rendering). When debug mode is enabled, additional options appear, such as Sobol-Burley. Note that the scrambling distance option is not compatible with the blue-noise pattern. Pull Request: https://projects.blender.org/blender/blender/pulls/118479
2024-06-05 02:29:47 +02:00
/*
* Performs base-4 Owen scrambling on a reversed-bit unsigned integer.
*
* See https://psychopath.io/post/2022_08_14_a_fast_hash_for_base_4_owen_scrambling
*/
ccl_device_inline uint reversed_bit_owen_base4(uint n, const uint seed)
Cycles: Implement blue-noise dithered sampling This patch implements blue-noise dithered sampling as described by Nathan Vegdahl (https://psychopath.io/post/2022_07_24_owen_scrambling_based_dithered_blue_noise_sampling), which in turn is based on "Screen-Space Blue-Noise Diffusion of Monte Carlo Sampling Error via Hierarchical Ordering of Pixels"(https://repository.kaust.edu.sa/items/1269ae24-2596-400b-a839-e54486033a93). The basic idea is simple: Instead of generating independent sequences for each pixel by scrambling them, we use a single sequence for the entire image, with each pixel getting one chunk of the samples. The ordering across pixels is determined by hierarchical scrambling of the pixel's position along a space-filling curve, which ends up being pretty much the same operation as already used for the underlying sequence. This results in a more high-frequency noise distribution, which appears smoother despite not being less noisy overall. The main limitation at the moment is that the improvement is only clear if the full sample amount is used per pixel, so interactive preview rendering and adaptive sampling will not receive the benefit. One exception to this is that when using the new "Automatic" setting, the first sample in interactive rendering will also be blue-noise-distributed. The sampling mode option is now exposed in the UI, with the three options being Blue Noise (the new mode), Classic (the previous Tabulated Sobol method) and the new default, Automatic (blue noise, with the additional property of ensuring the first sample is also blue-noise-distributed in interactive rendering). When debug mode is enabled, additional options appear, such as Sobol-Burley. Note that the scrambling distance option is not compatible with the blue-noise pattern. Pull Request: https://projects.blender.org/blender/blender/pulls/118479
2024-06-05 02:29:47 +02:00
{
n ^= n * 0x3d20adea;
n ^= (n >> 1) & (n << 1) & 0x55555555;
n += seed;
n *= (seed >> 16) | 1;
n ^= (n >> 1) & (n << 1) & 0x55555555;
n ^= n * 0x05526c56;
n ^= n * 0x53a22864;
return n;
}
/*
Cycles: improve Progressive Multi-Jittered sampling Fix two issues in the previous implementation: * Only power-of-two prefixes were progressively stratified, not suffixes. This resulted in unnecessarily increased noise when using non-power-of-two sample counts. * In order to try to get away with just a single sample pattern, the code used a combination of sample index shuffling and Cranley-Patterson rotation. Index shuffling is normally fine, but due to the sample patterns themselves not being quite right (as described above) this actually resulted in additional increased noise. Cranley-Patterson, on the other hand, always increases noise with randomized (t,s) nets like PMJ02, and should be avoided with these kinds of sequences. Addressed with the following changes: * Replace the sample pattern generation code with a much simpler algorithm recently published in the paper "Stochastic Generation of (t, s) Sample Sequences". This new implementation is easier to verify, produces fully progressively stratified PMJ02, and is *far* faster than the previous code, being O(N) in the number of samples generated. * It keeps the sample index shuffling, which works correctly now due to the improved sample patterns. But it now uses a newer high-quality hash instead of the original Laine-Karras hash. * The scrambling distance feature cannot (to my knowledge) be implemented with any decorrelation strategy other than Cranley-Patterson, so Cranley-Patterson is still used when that feature is enabled. But it is now disabled otherwise, since it increases noise. * In place of Cranley-Patterson, multiple independent patterns are generated and randomly chosen for different pixels and dimensions as described in the original PMJ paper. In this patch, the pattern selection is done via hash-based shuffling to ensure there are no repeats within a single pixel until all patterns have been used. The combination of these fixes brings the quality of Cycles' PMJ sampler in line with the previously submitted Sobol-Burley sampler in D15679. They are essentially indistinguishable in terms of quality/noise, which is expected since they are both randomized (0,2) sequences. Differential Revision: https://developer.blender.org/D15746
2022-08-23 20:48:48 +02:00
* Performs base-2 Owen scrambling on an unsigned integer.
*/
ccl_device_inline uint nested_uniform_scramble(const uint i, const uint seed)
{
Cycles: improve Progressive Multi-Jittered sampling Fix two issues in the previous implementation: * Only power-of-two prefixes were progressively stratified, not suffixes. This resulted in unnecessarily increased noise when using non-power-of-two sample counts. * In order to try to get away with just a single sample pattern, the code used a combination of sample index shuffling and Cranley-Patterson rotation. Index shuffling is normally fine, but due to the sample patterns themselves not being quite right (as described above) this actually resulted in additional increased noise. Cranley-Patterson, on the other hand, always increases noise with randomized (t,s) nets like PMJ02, and should be avoided with these kinds of sequences. Addressed with the following changes: * Replace the sample pattern generation code with a much simpler algorithm recently published in the paper "Stochastic Generation of (t, s) Sample Sequences". This new implementation is easier to verify, produces fully progressively stratified PMJ02, and is *far* faster than the previous code, being O(N) in the number of samples generated. * It keeps the sample index shuffling, which works correctly now due to the improved sample patterns. But it now uses a newer high-quality hash instead of the original Laine-Karras hash. * The scrambling distance feature cannot (to my knowledge) be implemented with any decorrelation strategy other than Cranley-Patterson, so Cranley-Patterson is still used when that feature is enabled. But it is now disabled otherwise, since it increases noise. * In place of Cranley-Patterson, multiple independent patterns are generated and randomly chosen for different pixels and dimensions as described in the original PMJ paper. In this patch, the pattern selection is done via hash-based shuffling to ensure there are no repeats within a single pixel until all patterns have been used. The combination of these fixes brings the quality of Cycles' PMJ sampler in line with the previously submitted Sobol-Burley sampler in D15679. They are essentially indistinguishable in terms of quality/noise, which is expected since they are both randomized (0,2) sequences. Differential Revision: https://developer.blender.org/D15746
2022-08-23 20:48:48 +02:00
return reverse_integer_bits(reversed_bit_owen(reverse_integer_bits(i), seed));
}
Cycles: Implement blue-noise dithered sampling This patch implements blue-noise dithered sampling as described by Nathan Vegdahl (https://psychopath.io/post/2022_07_24_owen_scrambling_based_dithered_blue_noise_sampling), which in turn is based on "Screen-Space Blue-Noise Diffusion of Monte Carlo Sampling Error via Hierarchical Ordering of Pixels"(https://repository.kaust.edu.sa/items/1269ae24-2596-400b-a839-e54486033a93). The basic idea is simple: Instead of generating independent sequences for each pixel by scrambling them, we use a single sequence for the entire image, with each pixel getting one chunk of the samples. The ordering across pixels is determined by hierarchical scrambling of the pixel's position along a space-filling curve, which ends up being pretty much the same operation as already used for the underlying sequence. This results in a more high-frequency noise distribution, which appears smoother despite not being less noisy overall. The main limitation at the moment is that the improvement is only clear if the full sample amount is used per pixel, so interactive preview rendering and adaptive sampling will not receive the benefit. One exception to this is that when using the new "Automatic" setting, the first sample in interactive rendering will also be blue-noise-distributed. The sampling mode option is now exposed in the UI, with the three options being Blue Noise (the new mode), Classic (the previous Tabulated Sobol method) and the new default, Automatic (blue noise, with the additional property of ensuring the first sample is also blue-noise-distributed in interactive rendering). When debug mode is enabled, additional options appear, such as Sobol-Burley. Note that the scrambling distance option is not compatible with the blue-noise pattern. Pull Request: https://projects.blender.org/blender/blender/pulls/118479
2024-06-05 02:29:47 +02:00
/*
* Performs base-4 Owen scrambling on an unsigned integer.
*/
ccl_device_inline uint nested_uniform_scramble_base4(const uint i, const uint seed)
Cycles: Implement blue-noise dithered sampling This patch implements blue-noise dithered sampling as described by Nathan Vegdahl (https://psychopath.io/post/2022_07_24_owen_scrambling_based_dithered_blue_noise_sampling), which in turn is based on "Screen-Space Blue-Noise Diffusion of Monte Carlo Sampling Error via Hierarchical Ordering of Pixels"(https://repository.kaust.edu.sa/items/1269ae24-2596-400b-a839-e54486033a93). The basic idea is simple: Instead of generating independent sequences for each pixel by scrambling them, we use a single sequence for the entire image, with each pixel getting one chunk of the samples. The ordering across pixels is determined by hierarchical scrambling of the pixel's position along a space-filling curve, which ends up being pretty much the same operation as already used for the underlying sequence. This results in a more high-frequency noise distribution, which appears smoother despite not being less noisy overall. The main limitation at the moment is that the improvement is only clear if the full sample amount is used per pixel, so interactive preview rendering and adaptive sampling will not receive the benefit. One exception to this is that when using the new "Automatic" setting, the first sample in interactive rendering will also be blue-noise-distributed. The sampling mode option is now exposed in the UI, with the three options being Blue Noise (the new mode), Classic (the previous Tabulated Sobol method) and the new default, Automatic (blue noise, with the additional property of ensuring the first sample is also blue-noise-distributed in interactive rendering). When debug mode is enabled, additional options appear, such as Sobol-Burley. Note that the scrambling distance option is not compatible with the blue-noise pattern. Pull Request: https://projects.blender.org/blender/blender/pulls/118479
2024-06-05 02:29:47 +02:00
{
return reverse_integer_bits(reversed_bit_owen_base4(reverse_integer_bits(i), seed));
}
ccl_device_inline uint expand_bits(uint x)
{
x &= 0x0000ffff;
x = (x ^ (x << 8)) & 0x00ff00ff;
x = (x ^ (x << 4)) & 0x0f0f0f0f;
x = (x ^ (x << 2)) & 0x33333333;
x = (x ^ (x << 1)) & 0x55555555;
return x;
}
ccl_device_inline uint morton2d(const uint x, const uint y)
Cycles: Implement blue-noise dithered sampling This patch implements blue-noise dithered sampling as described by Nathan Vegdahl (https://psychopath.io/post/2022_07_24_owen_scrambling_based_dithered_blue_noise_sampling), which in turn is based on "Screen-Space Blue-Noise Diffusion of Monte Carlo Sampling Error via Hierarchical Ordering of Pixels"(https://repository.kaust.edu.sa/items/1269ae24-2596-400b-a839-e54486033a93). The basic idea is simple: Instead of generating independent sequences for each pixel by scrambling them, we use a single sequence for the entire image, with each pixel getting one chunk of the samples. The ordering across pixels is determined by hierarchical scrambling of the pixel's position along a space-filling curve, which ends up being pretty much the same operation as already used for the underlying sequence. This results in a more high-frequency noise distribution, which appears smoother despite not being less noisy overall. The main limitation at the moment is that the improvement is only clear if the full sample amount is used per pixel, so interactive preview rendering and adaptive sampling will not receive the benefit. One exception to this is that when using the new "Automatic" setting, the first sample in interactive rendering will also be blue-noise-distributed. The sampling mode option is now exposed in the UI, with the three options being Blue Noise (the new mode), Classic (the previous Tabulated Sobol method) and the new default, Automatic (blue noise, with the additional property of ensuring the first sample is also blue-noise-distributed in interactive rendering). When debug mode is enabled, additional options appear, such as Sobol-Burley. Note that the scrambling distance option is not compatible with the blue-noise pattern. Pull Request: https://projects.blender.org/blender/blender/pulls/118479
2024-06-05 02:29:47 +02:00
{
return (expand_bits(x) << 1) | expand_bits(y);
}
CCL_NAMESPACE_END