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.. _normal_estimation_using_integral_images:
Normal Estimation Using Integral Images
---------------------------------------
In this tutorial we will learn how to compute normals for an organized point
cloud using integral images.
The code
--------
First, create a file, let's say, ``normal_estimation_using_integral_images.cpp`` in your favorite
editor, and place the following inside it:
.. code-block:: cpp
:linenos:
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>
#include <pcl/features/integral_image_normal.h>
#include <pcl/visualization/cloud_viewer.h>
int
main ()
{
// load point cloud
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::loadPCDFile ("table_scene_mug_stereo_textured.pcd", *cloud);
// estimate normals
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);
pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setNormalEstimationMethod (ne.AVERAGE_3D_GRADIENT);
ne.setMaxDepthChangeFactor(0.02f);
ne.setNormalSmoothingSize(10.0f);
ne.setInputCloud(cloud);
ne.compute(*normals);
// visualize normals
pcl::visualization::PCLVisualizer viewer("PCL Viewer");
viewer.setBackgroundColor (0.0, 0.0, 0.5);
viewer.addPointCloudNormals<pcl::PointXYZ,pcl::Normal>(cloud, normals);
while (!viewer.wasStopped ())
{
viewer.spinOnce ();
}
return 0;
}
The explanation
---------------
Now, let's break down the code piece by piece. In the first part we load a
point cloud from a file:
.. code-block:: cpp
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::loadPCDFile ("table_scene_mug_stereo_textured.pcd", *cloud);
In the second part we create an object for the normal estimation and compute
the normals:
.. code-block:: cpp
// estimate normals
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);
pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setNormalEstimationMethod (ne.AVERAGE_3D_GRADIENT);
ne.setMaxDepthChangeFactor(0.02f);
ne.setNormalSmoothingSize(10.0f);
ne.setInputCloud(cloud);
ne.compute(*normals);
The following normal estimation methods are available:
.. code-block:: cpp
enum NormalEstimationMethod
{
COVARIANCE_MATRIX,
AVERAGE_3D_GRADIENT,
AVERAGE_DEPTH_CHANGE
};
The COVARIANCE_MATRIX mode creates 9 integral images to compute the normal for
a specific point from the covariance matrix of its local neighborhood. The
AVERAGE_3D_GRADIENT mode creates 6 integral images to compute smoothed versions
of horizontal and vertical 3D gradients and computes the normals using the
cross-product between these two gradients. The AVERAGE_DEPTH_CHANGE mode
creates only a single integral image and computes the normals from the average
depth changes.
In the last part we visualize the point cloud and the corresponding normals:
.. code-block:: cpp
// visualize normals
pcl::visualization::PCLVisualizer viewer("PCL Viewer");
viewer.setBackgroundColor (0.0, 0.0, 0.5);
viewer.addPointCloudNormals<pcl::PointXYZ,pcl::Normal>(cloud, normals);
while (!viewer.wasStopped ())
{
viewer.spinOnce ();
}
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