1 | #include "common.h"
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2 | #include "SampleGenerator.h"
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3 |
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4 |
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5 | using namespace std;
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6 |
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7 | HaltonSequence SphericalSampleGenerator::sHalton;
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8 | HaltonSequence PoissonDiscSampleGenerator::sHalton;
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9 | HaltonSequence GaussianSampleGenerator::sHalton;
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10 |
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11 |
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12 | SampleGenerator::SampleGenerator(int numSamples, float radius):
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13 | mNumSamples(numSamples), mRadius(radius)
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14 | {}
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15 |
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16 |
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17 | PoissonDiscSampleGenerator::PoissonDiscSampleGenerator(int numSamples, float radius):
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18 | SampleGenerator(numSamples, radius)
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19 | {}
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20 |
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21 |
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22 | void PoissonDiscSampleGenerator::Generate(float *samples) const
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23 | {
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24 | // this is a hacky poisson sampling generator which does random dart-throwing
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25 | // until it is not able to place any dart for a number of tries
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26 | // in this case, the required min distance is reduced
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27 | // the solution is a possion sampling with respect to the adjusted min distance
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28 | // better solutions have been proposed, i.e., using hierarchical sampling
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29 |
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30 | const float maxTries = 1000;
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31 | const float f_reduction = 0.9f;
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32 |
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33 | static HaltonSequence halton;
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34 | float r[2];
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35 |
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36 | // generates poisson distribution on disc
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37 | float minDist = 2.0f / sqrt((float)mNumSamples);
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38 |
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39 | //cout << "minDist before= " << minDist << endl;
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40 | Sample2 *s = (Sample2 *)samples;
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41 |
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42 | for (int i = 0; i < mNumSamples; ++ i)
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43 | {
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44 | int tries = 0, totalTries = 0;
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45 |
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46 | // repeat until valid sample was found
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47 | while (1)
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48 | {
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49 | ++ tries;
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50 | ++ totalTries;
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51 |
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52 | halton.GetNext(2, r);
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53 |
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54 | const float rx = r[0] * 2.0f - 1.0f;
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55 | const float ry = r[1] * 2.0f - 1.0f;
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56 |
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57 | // check if in disk, else exit early
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58 | if (rx * rx + ry * ry > 1)
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59 | continue;
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60 |
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61 | bool sampleValid = true;
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62 |
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63 | // check poisson property
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64 | for (int j = 0; ((j < i) && sampleValid); ++ j)
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65 | {
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66 | const float dist =
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67 | sqrt((s[j].x - rx) * (s[j].x - rx) +
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68 | (s[j].y - ry) * (s[j].y - ry));
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69 |
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70 | if (dist < minDist)
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71 | sampleValid = false;
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72 | }
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73 |
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74 | if (sampleValid)
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75 | {
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76 | s[i].x = rx;
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77 | s[i].y = ry;
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78 | break;
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79 | }
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80 |
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81 | if (tries > maxTries)
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82 | {
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83 | minDist *= f_reduction;
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84 | tries = 0;
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85 | }
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86 | }
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87 |
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88 | //cout << "sample: " << samples[i].x << " " << i << " " << samples[i].y << " r: " << sqrt(samples[i].x * samples[i].x + samples[i].y * samples[i].y) << " tries: " << totalTries << endl;
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89 | }
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90 |
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91 | //cout << "minDist after= " << minDist << endl;
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92 | }
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93 |
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94 |
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95 |
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96 | GaussianSampleGenerator::GaussianSampleGenerator(int numSamples, float radius):
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97 | SampleGenerator(numSamples, radius)
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98 | {}
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99 |
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100 |
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101 | void GaussianSampleGenerator::Generate(float *samples) const
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102 | {
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103 | float r[2];
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104 |
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105 | const float sigma = mRadius;
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106 |
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107 | Sample2 *s = (Sample2 *)samples;
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108 |
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109 | const float p0 = 1.0f / (sigma * sqrt(2.0f * M_PI));
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110 | const float p1 = 1.0f / (sigma * sqrt(2.0f * M_PI));
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111 |
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112 | for (int i = 0; i < mNumSamples; ++ i)
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113 | {
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114 | sHalton.GetNext(2, r);
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115 |
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116 | float exp_x = -(r[0] * r[0]) / (2.0f * sigma * sigma);
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117 | float exp_y = -(r[1] * r[1]) / (2.0f * sigma * sigma);
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118 |
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119 | s[i].x = p0 * pow(M_E, exp_x);
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120 | s[i].y = p1 * pow(M_E, exp_y);
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121 | }
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122 |
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123 | //cout << "minDist after= " << minDist << endl;
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124 | }
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125 |
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126 |
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127 | SphericalSampleGenerator::SphericalSampleGenerator(int numSamples, float radius):
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128 | SampleGenerator(numSamples, radius)
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129 | {}
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130 |
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131 |
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132 | void SphericalSampleGenerator::Generate(float *samples) const
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133 | {
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134 | float r[2];
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135 | Sample3 *s = (Sample3 *)samples;
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136 |
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137 | // idea: use poisson distribution to generate samples
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138 | PoissonDiscSampleGenerator poisson(mNumSamples, 1.0f);
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139 | Sample2 *pSamples = new Sample2[mNumSamples];
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140 |
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141 | poisson.Generate((float *)pSamples);
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142 |
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143 | for (int i = 0; i < mNumSamples; ++ i)
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144 | {
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145 | //sHalton.GetNext(2, r);
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146 | r[0] = pSamples[i].x; r[1] = pSamples[i].y;
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147 |
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148 | // create stratified samples over sphere
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149 | const float theta = 2.0f * acos(sqrt(1.0f - r[0]));
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150 | const float phi = 2.0f * M_PI * r[1];
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151 |
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152 | s[i].x = mRadius * sin(theta) * cos(phi);
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153 | s[i].y = mRadius * sin(theta) * sin(phi);
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154 | s[i].z = mRadius * cos(theta);
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155 | }
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156 |
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157 | delete [] pSamples;
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158 | } |
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