In this tutorial we will learn how to compute normals for an organized point cloud using integral images.
First, create a file, let’s say, normal_estimation.cpp in your favorite editor, and place the following inside it:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | #include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/features/integral_image_normal.h>
int
main (int argc, char** argv)
{
pcl::PointCloud<pcl::PointXYZ> cloud;
// ... fill point cloud...
cloud.width = 640;
cloud.height = 480;
cloud.points.resize (cloud.width * cloud.height);
for (int ri = 0; ri < cloud.height; ++ri)
{
for (int ci = 0; ci < cloud.width; ++ci)
{
const float depth = 0.2f*static_cast<float> (rand ()) / static_cast<float>(RAND_MAX) + 1.0f;
cloud.points (ri, ci).x = (ci - 320) * depth;
cloud.points (ri, ci).y = (ri - 240) * depth;
cloud.points (ri, ci).z = depth;
}
}
// Estimate normals
pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
pcl::PointCloud<pcl::Normal> normals;
ne.setNormalEstimationMethod (ne.AVERAGE_DEPTH_CHANGE);
ne.setMaxDepthChangeFactor(0.02f);
ne.setNormalSmoothingSize(10.0f);
ne.setInputCloud(cloud);
ne.compute(normals);
return (0);
}
|
Now, let’s break down the code piece by piece. In the first part we create a random point cloud for which we estimate the normals:
pcl::PointCloud<pcl::PointXYZ> cloud;
// ... fill point cloud...
cloud.width = 640;
cloud.height = 480;
cloud.points.resize (cloud.width*cloud.height);
for (int ri = 0; ri < cloud.height; ++ri)
{
for (int ci = 0; ci < cloud.width; ++ci)
{
const float depth = 0.2f*static_cast<float> (rand ()) / static_cast<float>(RAND_MAX) + 1.0f;
cloud.points (ri, ci).x = (ci - 320) * depth;
cloud.points (ri, ci).y = (ri - 240) * depth;
cloud.points (ri, ci).z = depth;
}
}
In the second part we create an object for the normal estimation and compute the normals:
// Estimate normals
pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
pcl::PointCloud<pcl::Normal> normals;
ne.setNormalEstimationMethod (ne.AVERAGE_DEPTH_CHANGE);
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.