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:

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     #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:

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:

// 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:

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:

// 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 ();
}