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author MrJuneJune <me@mrjunejune.com>
date Mon, 19 Jan 2026 17:33:18 -0800
parents 7a4e942814bc
children
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1 ---
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2 title: Optimizing Random Placement with Colour Noise
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3 description: Comparing white noise and blue noise for natural object placement in game development, with optimization techniques for real-time rendering.
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6 # Optimizing Random Placement with Colour Noise
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7
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8 ### Introduction
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10 When it comes to placing objects like grass in a scene, achieving a balance between randomness and natural-looking distribution is a challenge. Too much randomness, like white noise, results in unnatural clustering or gaps. On the other hand, a completely uniform pattern can look artificial. Nature strikes a balance, often governed by constraints such as nutrient availability or environmental factors.
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11
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12 In this blog, I’ll walk you through how I tackled this problem by comparing white noise and blue noise for object placement. I’ll also explore an optimization technique inspired by Casey Muratori’s insights to improve performance, making it feasible for rendering grass in real-time applications.
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14 ### Understanding White Noise vs. Blue Noise
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16 White noise is completely random: each point is placed without consideration of others. While this randomness is simple to implement, it results in visually chaotic patterns with undesirable clumping.
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17
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18 Blue noise introduces spatial constraints, ensuring a minimum distance between points. This results in a more natural, evenly spaced pattern—perfect for simulating real-world distributions like grass or tree placement.
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20 To visualize this difference, I implemented functions to generate both white and blue noise patterns and compared their results.
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23 ### Implementation Highlights
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24
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25 #### White Noise
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26
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27 The **whiteNoiseRandom** function generates points completely randomly within a bounding box:
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28
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29 ```cpp
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30 void whiteNoiseRandom(Vector2& top_left_corner, Vector2& bottom_right_corner, const int& sides, const float& inner_radius, Vector2 positions[], int& i) {
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31 std::random_device rd;
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32 std::mt19937 gen(rd());
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33 std::uniform_real_distribution<float> random_float(0, 1);
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34
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35 Vector2 start_point = {
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36 (random_float(gen) * (bottom_right_corner.x - top_left_corner.x)) + top_left_corner.x ,
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37 (random_float(gen) * (bottom_right_corner.y - top_left_corner.y)) + top_left_corner.y
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38 };
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39 positions[i] = { start_point.x, start_point.y };
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40 }
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41 ```
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42
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43 This is computationally the most efficient as there is no constraints on where it can be placed which mean it will often lead to clumping together.
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44
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45 <div class="center"> <img src="/public/white-noise-grass.png" /> </div>
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47 Above image is okay, but lacks realism. We can do better.
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48
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49
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50 #### Blue Noise
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51
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52 Blue noise requires additional checks to maintain a minimum distance between points, implemented as follows:
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53
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54 ```cpp
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55 void blueNoiseRandom(Vector2& top_left_corner, Vector2& bottom_right_corner, const int& sides, const float& inner_radius, Vector2 positions[], int& i) {
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56 std::random_device rd;
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57 std::mt19937 gen(rd());
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58 std::uniform_real_distribution<float> random_float(0, 1);
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59
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60 int attempts = 0;
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61 const int max_attempts = sides * sides;
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62
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63 while (attempts < max_attempts) {
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64 Vector2 start_point = { random_float(gen) * sides, random_float(gen) * sides };
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65 if (x >= top_left_corner.x && x < bottom_right_corner.y && y >= top_left_corner.y && y < bottom_right_corner.y) {
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66 bool added = true;
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67 for (int j = 0; j < i; j++) {
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68 float dx = x - positions[j].x;
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69 float dy = y - positions[j].y;
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70 if (std::sqrt(dx * dx + dy * dy) < inner_radius) {
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71 added = false;
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72 break;
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73 }
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74 }
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75 if (added) {
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76 positions[i] = { start_point.x, start_point.y };
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77 break;
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78 }
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79 }
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80 attempts++;
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81 }
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82 }
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83 ```
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84
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85 As you can see from below image, this approach improves visual quality...
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86
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87 <div class="center"> <img src="/public/blue-noise-random.png" /> </div>
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88
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89 ...but at the cost of computational complexity. As the number of points increases, available space becomes limited, requiring more attempts to place a point. This can take several seconds, which isn’t ideal.
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90
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91 <video width="100%" controls>
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92 <source src="/public/slow-blue.mp4" type="video/mp4" />
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93 </video>
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94
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95 ** It takes around 8 seconds for 800 points in 1000 x 800 with 30 radius which means it should be able to add 100 more points in theory **
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97
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98 ### Optimizing Blue Noise Generation
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99
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101 While researching approaches, I came across Casey Muratori’s work on Witness, where he introduced a local sampling optimization. Instead of evaluating the entire grid for each point, checks are limited to a smaller region around the current point. This reduces redundant calculations, especially in dense scenes. Here’s my implementation:
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102
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103 ```cpp
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104 void blueNoiseCircle(Vector2& top_left_corner, Vector2& bottom_right_corner, const int& sides, const float& inner_radius, const float& outer_radius,
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105 Vector2& starting_point, Vector2 positions[], int& i){
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106 float center_x = starting_point.x;
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107 float center_y = starting_point.y;
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108
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109 std::random_device rd;
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110 std::mt19937 gen(rd());
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111 std::uniform_real_distribution<float> angle_dist(0, 2 * M_PI);
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112 std::uniform_real_distribution<float> radius_dist(0, 1);
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113
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114 bool added = false;
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115 int attempts = 0;
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116 const int max_attempts = outer_radius*outer_radius*PI - inner_radius*inner_radius*PI;
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117
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118 while (attempts <= max_attempts) {
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119 float theta = angle_dist(gen);
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120 float random_scale = radius_dist(gen);
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121 float radius = std::sqrt(random_scale * (outer_radius * outer_radius -
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122 inner_radius * inner_radius) +
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123 inner_radius * inner_radius);
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124 float x = center_x + radius * std::cos(theta);
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125 float y = center_y + radius * std::sin(theta);
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126
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127 added = true;
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128 if (x > 0 && x < sides && y > 0 && y < sides) {
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129 for (int j = 0; j < i; j++) {
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130 Vector2 current_point = positions[j];
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131 float dx = x - current_point.x;
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132 float dy = y - current_point.y;
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133 if (std::sqrt(dx * dx + dy * dy) < inner_radius) {
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134 added = false;
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135 break;
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136 }
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137 }
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138 if (added) {
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139 positions[i] = { x, y };
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140 break;
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141 }
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142 }
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143
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144 attempts++;
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145 }
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146 if (!added) {
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147 blueNoiseRandom(top_left_corner, bottom_right_corner, sides, inner_radius, positions, i);
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148 }
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149 }
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150 ```
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151
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152 <video width="100%" controls>
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153 <source src="/public/fast-blue.mp4" type="video/mp4" />
100
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154 </video>
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155
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156 ** It only took 2 seconds or so. It slows down at the end since it probably needed to look for one or two points using random number **
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157
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158
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159 <div class="center"> <img src="/public/blue-noises-optimal.png" /> </div>
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160
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161
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162 ### Conclusion
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163
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164 If you're working on object placement for games, simulations, or any graphics project, understanding the balance between randomness and natural constraints is crucial. Blue noise offers a compelling way to achieve this balance, and with optimizations, it becomes practical for real-time rendering.
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165
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166 You can find the full source code for my implementation on [GitHub](https://github.com/MrJuneJune/blog_sample_codes/tree/main/performance_tests/noises).