# Filtering a PointCloud using ModelOutlierRemoval

This tutorial demonstrates how to extract parametric models for example for planes or spheres out of a PointCloud by using SAC_Models with known coefficients. If you don’t know the models coefficients take a look at the How to use Random Sample Consensus model tutorial.

# The code

First, create a file, let’s call it `model_outlier_removal.cpp`, in your favorite editor, and place the following inside it:

``` 1#include <iostream>
2#include <pcl/point_types.h>
3#include <pcl/filters/model_outlier_removal.h>
4
5int
6main ()
7{
8  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
9  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_sphere_filtered (new pcl::PointCloud<pcl::PointXYZ>);
10
11  // 1. Generate cloud data
12  std::size_t noise_size = 5;
13  std::size_t sphere_data_size = 10;
14  cloud->width = noise_size + sphere_data_size;
15  cloud->height = 1;
16  cloud->points.resize (cloud->width * cloud->height);
18  for (std::size_t i = 0; i < noise_size; ++i)
19  {
20    (*cloud)[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
21    (*cloud)[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
22    (*cloud)[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
23  }
25  double rand_x1 = 1;
26  double rand_x2 = 1;
27  for (std::size_t i = noise_size; i < (noise_size + sphere_data_size); ++i)
28  {
29    // See: http://mathworld.wolfram.com/SpherePointPicking.html
30    while (pow (rand_x1, 2) + pow (rand_x2, 2) >= 1)
31    {
32      rand_x1 = (rand () % 100) / (50.0f) - 1;
33      rand_x2 = (rand () % 100) / (50.0f) - 1;
34    }
35    double pre_calc = sqrt (1 - pow (rand_x1, 2) - pow (rand_x2, 2));
36    (*cloud)[i].x = 2 * rand_x1 * pre_calc;
37    (*cloud)[i].y = 2 * rand_x2 * pre_calc;
38    (*cloud)[i].z = 1 - 2 * (pow (rand_x1, 2) + pow (rand_x2, 2));
39    rand_x1 = 1;
40    rand_x2 = 1;
41  }
42
43  std::cerr << "Cloud before filtering: " << std::endl;
44  for (const auto& point: *cloud)
45    std::cout << "    " << point.x << " " << point.y << " " << point.z << std::endl;
46
47  // 2. filter sphere:
48  // 2.1 generate model:
49  // modelparameter for this sphere:
50  // position.x: 0, position.y: 0, position.z:0, radius: 1
51  pcl::ModelCoefficients sphere_coeff;
52  sphere_coeff.values.resize (4);
53  sphere_coeff.values = 0;
54  sphere_coeff.values = 0;
55  sphere_coeff.values = 0;
56  sphere_coeff.values = 1;
57
58  pcl::ModelOutlierRemoval<pcl::PointXYZ> sphere_filter;
59  sphere_filter.setModelCoefficients (sphere_coeff);
60  sphere_filter.setThreshold (0.05);
61  sphere_filter.setModelType (pcl::SACMODEL_SPHERE);
62  sphere_filter.setInputCloud (cloud);
63  sphere_filter.filter (*cloud_sphere_filtered);
64
65  std::cerr << "Sphere after filtering: " << std::endl;
66  for (const auto& point: *cloud_sphere_filtered)
67    std::cout << "    " << point.x << " " << point.y << " " << point.z << std::endl;
68
69  return (0);
70}
```

# The explanation

Now, let’s break down the code piece by piece.

In the following lines, we define the PointClouds structures, fill in noise, random points on a plane as well as random points on a sphere and display its content to screen.

```{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_sphere_filtered (new pcl::PointCloud<pcl::PointXYZ>);

// 1. Generate cloud data
std::size_t noise_size = 5;
std::size_t sphere_data_size = 10;
cloud->width = noise_size + sphere_data_size;
cloud->height = 1;
cloud->points.resize (cloud->width * cloud->height);
for (std::size_t i = 0; i < noise_size; ++i)
{
(*cloud)[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
(*cloud)[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
(*cloud)[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
}
double rand_x1 = 1;
double rand_x2 = 1;
for (std::size_t i = noise_size; i < (noise_size + sphere_data_size); ++i)
{
// See: http://mathworld.wolfram.com/SpherePointPicking.html
while (pow (rand_x1, 2) + pow (rand_x2, 2) >= 1)
{
rand_x1 = (rand () % 100) / (50.0f) - 1;
rand_x2 = (rand () % 100) / (50.0f) - 1;
}
double pre_calc = sqrt (1 - pow (rand_x1, 2) - pow (rand_x2, 2));
(*cloud)[i].x = 2 * rand_x1 * pre_calc;
(*cloud)[i].y = 2 * rand_x2 * pre_calc;
(*cloud)[i].z = 1 - 2 * (pow (rand_x1, 2) + pow (rand_x2, 2));
rand_x1 = 1;
rand_x2 = 1;
}

std::cerr << "Cloud before filtering: " << std::endl;
for (const auto& point: *cloud)
std::cout << "    " << point.x << " " << point.y << " " << point.z << std::endl;
```

Finally we extract the sphere using ModelOutlierRemoval.

```  // position.x: 0, position.y: 0, position.z:0, radius: 1
pcl::ModelCoefficients sphere_coeff;
sphere_coeff.values.resize (4);
sphere_coeff.values = 0;
sphere_coeff.values = 0;
sphere_coeff.values = 0;
sphere_coeff.values = 1;

pcl::ModelOutlierRemoval<pcl::PointXYZ> sphere_filter;
sphere_filter.setModelCoefficients (sphere_coeff);
sphere_filter.setThreshold (0.05);
sphere_filter.setModelType (pcl::SACMODEL_SPHERE);
```

# Compiling and running the program

``` 1cmake_minimum_required(VERSION 3.5 FATAL_ERROR)
2
3project(model_outlier_removal)
4
5find_package(PCL 1.7 REQUIRED)
6
7include_directories(\${PCL_INCLUDE_DIRS})
10
```

After you have made the executable, you can run it. Simply do:

```\$ ./model_outlier_removal
```

You will see something similar to:

```Cloud before filtering:
0.352222 -0.151883 -0.106395
-0.397406 -0.473106 0.292602
-0.731898 0.667105 0.441304
-0.734766 0.854581 -0.0361733
-0.4607 -0.277468 -0.916762
-0.82 -0.341666 0.4592
-0.728589 0.667873 0.152
-0.3134 -0.873043 -0.3736
0.62553 0.590779 0.5096
-0.54048 0.823588 -0.172
-0.707627 0.424576 0.5648
-0.83153 0.523556 0.1856
-0.513903 -0.719464 0.4672
0.291534 0.692393 0.66
0.258758 0.654505 -0.7104
Sphere after filtering:
-0.82 -0.341666 0.4592
-0.728589 0.667873 0.152
-0.3134 -0.873043 -0.3736
0.62553 0.590779 0.5096
-0.54048 0.823588 -0.172
-0.707627 0.424576 0.5648
-0.83153 0.523556 0.1856
-0.513903 -0.719464 0.4672
0.291534 0.692393 0.66
0.258758 0.654505 -0.7104
```