2023-06-14 16:52:36 +10:00
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/* SPDX-FileCopyrightText: 2011-2022 Blender Foundation
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*
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* SPDX-License-Identifier: Apache-2.0 */
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2022-08-18 20:45:09 +02:00
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#pragma once
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#include "util/types.h"
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CCL_NAMESPACE_BEGIN
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/*
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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
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* Performs base-2 Owen scrambling on a reversed-bit unsigned integer.
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2022-08-18 20:45:09 +02:00
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*
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* This is equivalent to the Laine-Karras permutation, but much higher
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* quality. See https://psychopath.io/post/2021_01_30_building_a_better_lk_hash
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*/
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ccl_device_inline uint reversed_bit_owen(uint n, uint seed)
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{
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n ^= n * 0x3d20adea;
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n += seed;
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n *= (seed >> 16) | 1;
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n ^= n * 0x05526c56;
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n ^= n * 0x53a22864;
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return n;
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}
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/*
|
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
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* Performs base-2 Owen scrambling on an unsigned integer.
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2022-08-18 20:45:09 +02:00
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*/
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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
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ccl_device_inline uint nested_uniform_scramble(uint i, uint seed)
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2022-08-18 20:45:09 +02:00
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{
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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
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return reverse_integer_bits(reversed_bit_owen(reverse_integer_bits(i), seed));
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2022-08-18 20:45:09 +02:00
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}
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CCL_NAMESPACE_END
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