1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
|
/* 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 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);
return x;
}
ccl_device float pmj_sample_1D(KernelGlobals kg, uint sample, uint rng_hash, uint dimension)
{
uint hash = rng_hash;
float jitter_x = 0.0f;
if (kernel_data.integrator.scrambling_distance < 1.0f) {
hash = kernel_data.integrator.seed;
jitter_x = hash_wang_seeded_float(dimension, rng_hash) *
kernel_data.integrator.scrambling_distance;
}
/* Perform Owen shuffle of the sample number to reorder the samples. */
const uint rv = hash_cmj_seeded_uint(dimension, hash);
#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. */
float dx = hash_cmj_seeded_float(d, hash);
/* 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;
if (kernel_data.integrator.scrambling_distance < 1.0f) {
hash = kernel_data.integrator.seed;
jitter_x = hash_wang_seeded_float(dimension, rng_hash) *
kernel_data.integrator.scrambling_distance;
jitter_y = hash_wang_seeded_float(dimension + 1, rng_hash) *
kernel_data.integrator.scrambling_distance;
}
/* Perform a shuffle on the sample number to reorder the samples. */
const uint rv = hash_cmj_seeded_uint(dimension, hash);
#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. */
float dx = hash_cmj_seeded_float(d, hash);
float dy = hash_cmj_seeded_float(d + 1, hash);
/* 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;
}
CCL_NAMESPACE_END
|
