# Cloud Normal Calculation. This post is a wiki. Anyone with karma >75 is welcome to improve it.

I want to see the cloud normal for a given point cloud in vector format means in position vector. Here is my sample code. From my code, I can found only the cloud normal points size.

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>

#include <pcl/surface/convex_hull.h>
#include <pcl/surface/mls.h>

#include <pcl/filters/filter.h>
#include <pcl/filters/voxel_grid.h>

#include <pcl/features/normal_3d.h>
#include <pcl/features/boundary.h>

#include <pcl/registration/distances.h>

#include <pcl/kdtree/kdtree_flann.h>

#include <Eigen/Dense>
#include <Eigen/Sparse>
#include <Eigen/Geometry>

using namespace Eigen;

int main (int argc, char** argv)
{
pcl::PointCloud<pcl::PointXYZ> cloud;

// Fill in the cloud data
cloud.width    = 5;
cloud.height   = 1;
cloud.is_dense = false;
cloud.points.resize (cloud.width * cloud.height);

for (size_t i = 0; i < cloud.points.size (); ++i)
{
cloud.points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);
cloud.points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);
cloud.points[i].z = 1024 * rand () / (RAND_MAX + 1.0f);
}

pcl::io::savePCDFileASCII ("test_pcd.pcd", cloud);
std::cerr << "Saved " << cloud.points.size () << " data points to test_pcd.pcd." << std::endl;

for (size_t i = 0; i < cloud.points.size (); ++i)
std::cerr << "    " << cloud.points[i].x << " " << cloud.points[i].y << " " << cloud.points[i].z << std::endl;

// Create the normal estimation class, and pass the input dataset to it

pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setInputCloud (cloud.makeShared());

// Create an empty kdtree representation, and pass it to the normal estimation object.
// Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ());
ne.setSearchMethod (tree);

// Output datasets
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);

// Use all neighbors in a sphere of radius 3cm

// Compute the features
ne.compute (*cloud_normals);

std::cerr << "Normal cloud size " << cloud_normals->points.size()<< std::endl;

}

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I'm not completely sure that I understand your question, but you can access the components of a normal for a given point using cloud_normals-&gt;points.at(i).normal. Here i would be the point number. I use Eigen::Map&lt;Eigen::Vector3f&gt; tmp(cloud_normals-&gt;points.at(i).normal); to get into Eigen

I'm not sure why &gt and &lt are showing up instead of the actual greater-than and less-than signs... Hopefully it is still readable

Hello, Jarvis, I have a problem do you know how to estimate a normal for a single point using the function:

computePointNormal (const pcl::PointCloud<pointint> &cloud, const std::vector<int> &indices, Eigen::Vector4f &plane_parameters, float &curvature);

How can I fill the indices?

After the normal calculation how can I compare two normals of different points and check if their pointing directions?

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To access the components of a normal for a given point, I use following code.

.................

for (size_t i = 0; i < cloud_normals->points.size(); ++i)
std::cerr << "    " << cloud_normals->points[i].normal_x << " " << cloud_normals->points[i].normal_y << " " << cloud_normals->points[i].normal_z << std::endl;

.................

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